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Neural Approaches to Conversational AI

Jianfeng Gao, Michel Galley, Lihong Li

arXiv:1809.08267v3cs.CL

TL;DR

Conversational AI needs systems that can answer questions, complete tasks, and sustain appropriate conversation across diverse settings. This survey organizes neural approaches to these problems, connects them with traditional methods, and reviews training strategies and representative systems. It concludes that dialogue can be framed as optimal decision making and that neural approaches have improved performance across tasks and domains, while important interpretability and grounding challenges remain.

  • Problem

    Conversational AI must support question answering, task completion, and social conversation, but the field still faces challenges in developing systems that fulfill these goals.

  • Method

    The paper surveys neural approaches across QA, task-oriented dialogue, and social chatbots, connects them with traditional approaches, and reviews supervised and reinforcement learning methods.

  • Results

    The survey concludes that dialogue systems can be conceptualized as optimal decision processes and that recent neural approaches significantly improve performance across tasks and domains.

  • Takeaways & Limitations

    Commercial dialogue systems commonly combine symbolic methods and neural models to provide conversational access to services and information.

  • Takeaways & Limitations

    Neural approaches remain limited in interpretability, and end-to-end systems continue to face grounding and response-appropriateness challenges.

Abstract

from arXiv · show

The present paper surveys neural approaches to conversational AI that have been developed in the last few years. We group conversational systems into three categories: (1) question answering agents, (2) task-oriented dialogue agents, and (3) chatbots. For each category, we present a review of state-of-the-art neural approaches, draw the connection between them and traditional approaches, and discuss the progress that has been made and challenges still being faced, using specific systems and models as case studies.

Introduction

The paper surveys neural conversational AI across question answering, task-oriented dialogue, and social chatbots, connecting modern neural methods with traditional approaches. It presents training approaches, system landscapes, progress, and continuing challenges through case studies.

  • Scope and organization: The survey covers question answering, task-oriented dialogue, and social chatbots within a unified view of conversational AI.It reviews neural approaches for each category and relates them to traditional approaches.
  • Scope and organization: The paper connects modern neural approaches with traditional approaches to clarify how conversational-AI research has evolved.This comparison is intended to illuminate how the field can move forward.
  • Methods: State-of-the-art dialogue-agent training approaches use both supervised learning and reinforcement learning.The paper presents these approaches as methods for training dialogue agents.
  • Dialogue problems: Dialogue systems address three problem types: answering questions, completing tasks, and conducting social chat.Examples include retrieving answers from diverse data sources, scheduling or planning, and conversing appropriately with users.
  • Dialogue systems: The unified framework includes task-oriented and chitchat bots, while large-scale systems can combine multiple conversation skills under hierarchical management.Sounding Board and XiaoIce are described as using a top-level manager with lower-level skills for different conversation segments.
  • Neural and traditional approaches: Neural and symbolic approaches offer complementary strengths: neural methods handle paraphrase variation and end-to-end training, whereas symbolic methods provide interpretability and execution efficiency.Hybrid methods combine strengths from both approaches.

Machine Learning Background

The chapter frames conversational AI as a learning problem and reviews supervised and reinforcement learning foundations, along with neural architectures that learn representations and support conversational tasks.

  • Dialogue as a learning problem: Conversational learning can be formulated as conversations fulfilling a user’s goal, with cumulative reward measured over sequences of user-agent interactions.
  • Supervised learning: Supervised learning trains a parameterized model on input-output pairs by minimizing a cost function, commonly using mini-batch stochastic gradient descent.The recipe includes a dataset, model, loss, and optimization procedure.
  • Reinforcement learning: Reinforcement learning is useful when desired dialogue behaviors are unavailable as representative labeled examples, requiring agents to learn through interaction with an environment.
  • Reinforcement learning: Dialogue reinforcement learning must address exploration-exploitation, delayed rewards, temporal credit assignment, and partially observed states.The agent selects actions from dialogue states while learning from rewards that may arrive only after a sequence of turns.
  • Learning challenges: Generalization to unseen inputs is a central challenge, and neural approaches potentially address it through the representation-learning capacity of deep networks.
  • Neural approaches: Deep learning jointly learns feature representations and classification, replacing hand-engineered features with a feature generator optimized end-to-end with the classifier.NLP architectures may use convolutional layers for local dependencies, recurrent layers for global sequences, and word embeddings to map vocabulary items into lower-dimensional vectors.

Question Answering and Machine Reading Comprehension

Question answering agents span KB-QA and text-QA: neural methods replace or extend symbolic processing with continuous representations, while conversational systems add multi-turn interaction. The chapter reviews their architectures, datasets, reasoning methods, benefits, and limitations.

  • KB-QA and text-QA: KB-QA agents let users query large knowledge bases interactively in natural language, while text-QA agents answer from document collections.KB-QA is more flexible than SQL-like systems; text-QA uses passages as the basis for answering questions.
  • Text-QA and MRC: Neural text-QA centers on machine reading comprehension models that encode questions and passages, reason in neural space, and generate answers.The chapter reviews MRC datasets and benchmarks, conversational extensions, and architectures for both single-turn and multi-turn text-QA.
  • KB-QA and text-QA: Symbolic KB-QA maps questions to logical forms and retrieves answers by matching paths in a knowledge base.Knowledge bases represent facts as subject-predicate-object triples, with entities as nodes and relations as directed edges.
  • KB-QA and text-QA: Symbolic systems face paraphrase mismatches and exponentially growing search over multi-step relation paths in large knowledge bases.These difficulties arise when natural-language predicates differ from knowledge-base labels and when complex queries require many candidate paths.
  • Neural KB-QA: Neural KB-QA methods map entities and relations into continuous semantic vectors, enabling inference over compact knowledge-base representations rather than explicit path search.The chapter presents this approach as a response to paraphrasing and search-complexity problems.
  • Benefits and limitations: Neural methods improve robustness to paraphrase and avoid explicit search over very large knowledge bases, but their reasoning is difficult to interpret symbolically.The chapter notes that neural controllers perform iterative memory lookups rather than explicitly traversing knowledge-base paths.
  • Conversational KB-QA: Neural KB-QA supports multi-turn agents such as KB-InfoBot, which replaces symbolic database queries with an induced posterior over entries and integrates induction with reinforcement learning.Experiments reported for KB-InfoBot show higher task success rate and reward in simulations and against real users.
  • Text-QA and MRC: Dynamic multi-step reasoning outperforms fixed multi-step and single-step reasoning on SQuAD and MS MARCO, but requires reinforcement-learning methods that can be unstable to implement.MS MARCO is more challenging than SQuAD because it uses real queries, web documents, unanswerable queries, and generated answers from multiple passages.

Task-oriented Dialogue Systems

Task-oriented dialogue systems help users complete specific goals, such as reservations or ticket bookings, and are typically domain dependent. This chapter reviews their foundations, algorithms, evaluation, and core components for raw-text interactions.

  • Task-oriented dialogues assist users in completing a specific task rather than simply seeking information or chatting.Examples include making hotel reservations and booking movie tickets.
  • These systems typically pursue a defined goal and operate within a particular domain.
  • The chapter focuses on foundational and algorithmic aspects, while treating industrial applications separately.
  • The discussion is restricted to dialogues whose user input is raw text rather than spoken language.
  • Its organization covers basic concepts, evaluation approaches, and three main components of a typical dialogue system.Evaluation is described as critical to development but largely orthogonal to the concrete construction techniques.

4.1 Overview

The overview introduces task-oriented dialogue systems and directs readers to prior textbook material for broader information and historical development.

  • The chapter begins by reviewing task-oriented dialogue systems to support the discussions that follow.
  • For additional information and historical developments, readers are referred to Jurafsky and Martin (2018), Chapters 25 and 26.

Slot-filling Dialogues

Slot-filling dialogues require collecting values for a predefined set of domain-specific slots before a task can be completed. Slots may constrain conversation, be requested by the user, or serve both roles.

  • Slot-filling dialogues collect values for predefined slots needed to complete a task.They are used in practical transactions such as booking tickets.
  • Domain experts define application-specific slots for domains such as movies, restaurants, and air-ticket booking.Movie-booking slots include movie name, theater name, time, date, ticket price, and number of tickets.
  • An informable slot has a value that constrains the conversation, whereas a requestable slot is one whose value the speaker can ask for.Phone number is an example of an informable slot, while ticket price is requestable.
  • A slot can be both informable and requestable, as with movie name.

Dialogue Acts

Dialogue acts provide a formal representation of user and system utterances as actions that change conversational state. They encode functions such as greeting, requesting information, and informing slot values, with inventories that vary by domain.

  • User utterances act as observations and system utterances as actions, paralleling an agent’s interaction with an environment in reinforcement learning.
  • Dialogue acts represent utterances as actions that can change the mental state of both participants and the conversation.
  • A greet act corresponds to natural-language greetings that start a conversation.
  • A question such as “How many tickets do you need?” requests information about a slot.
  • The utterance “I want to watch it in Seattle” is represented as informing the city value.
  • Dialogue-act inventories are domain specific, so movie and restaurant domains use different sets of acts.

Dialogue as Optimal Decision Making

Multi-turn dialogue can be formulated as reinforcement learning: the system tracks dialogue state, acts, receives user responses, and is rewarded for dialogue quality or cost. A practical pipeline separates understanding, state tracking, policy selection, and generation, with possible database access.

  • In reinforcement learning, the dialogue system is the agent and the user is the environment in a turn-by-turn interaction.The system updates its dialogue state and selects an action before the user produces the next utterance.
  • Each turn updates the agent’s internal dialogue state, selects a dialogue act or internal operation, and computes an immediate reward.Actions may include responses, database lookups, or API calls.
  • A practical multi-turn pipeline uses NLU, a dialogue manager containing DST and policy modules, and NLG, with optional database access.NLU converts raw utterances to dialogue acts; DST tracks state and the policy selects actions from it.
  • Task-oriented rewards can combine successful task completion with a penalty for intermediate turns.The paper gives +20 for successful completion, −20 otherwise, and −1 for each intermediate turn.

Dialogue Manager

Traditional dialogue managers frame conversation as decision making but often assume finite, fully observable states or require engineered domain knowledge. Separately optimized modules also make it difficult to identify which component causes poor responses, motivating end-to-end neural and reinforcement-learning approaches.

  • Early reinforcement-learning dialogue managers assumed finitely many possible states and perfect dialogue-state tracking.Ambiguous user utterances and unavoidable NLU errors often violate both assumptions in real-world applications.
  • POMDPs model uncertainty by maintaining a posterior belief state over unobserved dialogue states from user-utterance observations.Exact POMDP optimization is computationally intractable, so prior work studied approximations and alternative state representations.
  • Traditional approaches often require more engineered domain knowledge for feature construction and state design than later neural approaches.
  • Separately optimizing dialogue modules makes credit assignment difficult when the system produces an undesirable response.Differentiable neural models combined with reinforcement learning allow end-to-end optimization, potentially improving conversation quality.

4.2 Evaluation and User Simulation

Dialogue evaluation combines task success, conversational cost, and other quality dimensions, while user simulation offers inexpensive RL interaction but remains imperfect. Because simulated and human users differ, reliable evaluation may require testing with people.

  • Evaluation: Whole-dialogue evaluation is more challenging than component evaluation because reward functions must represent multiple aspects of dialogue quality.Component metrics include accuracy, precision, recall, F1, and BLEU, whereas whole systems require a more holistic view.
  • Evaluation: Task success rate measures the fraction of dialogues that successfully solve the user’s problem.Its example reward is 0 on every turn, then +1 for successful completion or −1 otherwise on the final turn.
  • Evaluation: Number of turns measures dialogue cost, assigning a simple −1 reward per turn to favor more succinct conversations.
  • Evaluation: Reward functions may also encode chatbot coherence, diversity, and personal style, although this direction is relatively under-investigated.
  • User Simulation: Simulated users let reinforcement-learning algorithms interact with users at virtually no cost.A simulator maintains dialogue state and converses with the RL dialogue system.
  • User Simulation: Agenda-based simulators encode pending user intentions in a stack whose first-in-last-out operations implicitly determine priorities.The agenda represents conversation history and the user’s state of mind.
  • User Simulation: Model-based simulators use conversation contexts with LSTM or sequence-to-sequence models to generate the next user utterance.They can be trained on human-human dialogue corpora and combined with rule-based techniques.
  • User Simulation: Policies overfitted to one user simulator may perform poorly with another simulator or real humans, making human-user testing necessary for reliable quality evaluation.The paper notes that simulator evaluation itself remains an ongoing research topic.

4.3 Natural Language Understanding and Dialogue State Tracking

NLU and dialogue-state tracking are essential, closely related components whose outputs affect subsequent dialogue decisions. Neural methods address classification, sequence tagging, contextual understanding, joint and cross-domain learning, and belief-state prediction.

  • Natural Language Understanding: NLU converts user utterances into semantic dialogue acts through domain detection, intent determination, and slot tagging.These tasks are commonly evaluated with accuracy and F1, and NLU quality affects later dialogue modules.
  • Natural Language Understanding: Neural recurrent models have outperformed traditional statistical methods on domain and intent classification and on slot tagging.Reported comparisons include recurrent models against statistical methods such as conditional random fields and support vector machines.
  • Natural Language Understanding: Bidirectional LSTMs process both forward and reversed input sequences, then concatenate their hidden layers for output computation.The forward LSTM uses input, forget, and output gates; its backward counterpart processes the reversed sequence.
  • Natural Language Understanding: Context from previous utterances can improve NLU when the current utterance is ambiguous or incomplete.Prior work models conversation history as role-alternating word sequences or uses memory networks to select contextual information.
  • Natural Language Understanding: Jointly solving NLU tasks across domains can reduce the labeled data needed to build models for a new domain.Zero-shot approaches represent slots from different domains in a shared latent semantic space using slot-description embeddings.
  • Dialogue State Tracking: Dialogue state records what the user seeks at the current turn and supplies the input used by the dialogue policy to choose the next action.A restaurant state can include slot constraints, requested slots, and the current dialogue search method.
  • Dialogue State Tracking: Neural dialogue-state trackers have progressed from expert-built or statistical models to deep and recurrent networks, including the Neural Belief Tracker.The Neural Belief Tracker encodes the latest system and user utterances and predicts candidate slot-value pairs through a softmax layer.
  • Dialogue State Tracking: Belief spans represent informable and requestable slot values gathered during a conversation and facilitate neural sequence-to-sequence dialogue learning.

4.4 Dialogue Policy Learning

This section surveys reinforcement-learning approaches for dialogue policy optimization, including DQN-based methods, exploration strategies, warm starts, and simulated-user planning. It also highlights challenges from large multi-domain state spaces and imperfect user simulation.

  • Policy Optimization: Dialogue policies can be optimized with online reinforcement learning from user interactions or batch reinforcement learning from fixed transition data.The section focuses on the online setting, often combined with batch learning.
  • DQN: DQN encodes the dialogue state, outputs Q-values for possible dialogue-act and slot actions, and selects actions using the highest-valued output.The network is trained with standard reinforcement-learning algorithms and experience replay.
  • Warm-start Policy: Warm starts accelerate policy learning by restricting policy search with expert dialogues, teacher advice, or an initialized reasonable policy.Learning a good policy from scratch often requires substantial data.
  • Efficient Exploration and Domain Extension: BBQ improves exploration by maintaining a posterior over neural-network weights, sampling weights, and selecting the action with the highest sampled value.Experiments show more efficient exploration than state-of-the-art baselines for dialogue domain extension.
  • Multi-domain Dialogues: Composite and multi-domain tasks enlarge the state space because subtasks may share slot constraints or require reusable policies across related domains.These properties make direct reinforcement-learning optimization inefficient.
  • Integration of Planning and Learning: Deep Dyna-Q combines real-user interaction, user-model refinement, and reinforcement-learning planning against simulated users to improve sample efficiency.Human-in-the-loop experiments show that DDQ efficiently improves dialogue policies through real-user interaction.
  • Integration of Planning and Learning: DDQ must balance real-user samples with simulated-user samples, motivating heuristics and discriminator-based alternatives such as D3Q.The cited heuristic reduces planning steps when more real interactions become available.

4.5 Natural Language Generation

Natural language generation converts dialogue-manager goals into natural-language responses, but handcrafted templates are costly to maintain. Neural methods address this challenge through data-driven generation, including semantically controlled recurrent models.

  • Motivation: Natural language generation converts a dialogue manager’s communication goal into a natural-language response and affects dialogue naturalness and user experience.NLG is one component of a conversational system’s response-generation process.
  • Motivation: Template- and rule-based generation requires domain experts to write and maintain templates and selection heuristics, making adaptation to new domains challenging.These costs motivate corpus-based and other data-driven approaches.
  • Corpus-based Methods: Corpus-based generation optimizes a generation module from corpora, typically with supervised learning, while some approaches use reinforcement learning to trade off sentence length and information revealed.The decision-theoretic formulation treats generation as an optimization problem.
  • SC-LSTM: SC-LSTM extends an LSTM with a sentence-planning component that provides semantic control over generated language.The architecture combines a typical LSTM cell with a sentence planning cell.
  • SC-LSTM: SC-LSTM’s reading gate computes a sequence of dialogue acts from the original act and controls information flow so generated utterances represent intended meaning.The remaining gates follow the standard LSTM structure.
  • SC-LSTM: SC-LSTM can be deepened by stacking cells and improved with utterance reranking using a model trained on the reversed input sequence.These extensions combine multiple model instances to finalize reranking.
  • Further Extensions: Later work explores multi-domain learning to reduce training data requirements and hierarchical generation that leverages linguistic patterns.Language generation remains an active research area.

4.6 End-to-end Learning

End-to-end learning addresses the limitations of separately optimized dialogue modules by training differentiable components jointly. The surveyed approaches use supervised learning, reinforcement learning, or hybrids, while knowledge-base access remains a key design issue.

  • Motivation: Separately optimized dialogue modules complicate system design, and improvements in individual modules do not necessarily improve the whole dialogue system.Joint optimization was argued to benefit dialogue management and natural-language generation.
  • End-to-end Architecture: Differentiable neural components allow whole dialogue systems to be optimized by back-propagation against metrics of overall system quality.This contrasts with traditional approaches that optimize components separately.
  • Supervised Learning: Supervised end-to-end learning trains dialogue components on desired system responses to maximize prediction accuracy.The approach requires response data collected for training.
  • Knowledge-base Access: Non-differentiable knowledge-base lookups can force separate supervised training, whereas soft knowledge-base lookups address this differentiability challenge.Modular neural systems may otherwise retain symbolic lookup operations.
  • Supervised Learning: Memory networks, embedding models, pointer mechanisms, and key-value retrieval networks incorporate dialogue history or external knowledge-base information into response generation.Reported systems include memory networks, Mem2Seq, and an attention-based key-value retrieval network.
  • Reinforcement Learning: Reinforcement learning complements supervised learning because it can explore policies beyond expert responses, although supervised data may be expensive to obtain.The section surveys reinforcement-learning approaches to end-to-end dialogue systems.
  • Reinforcement Learning: End-to-end reinforcement-learning models can jointly optimize policy decisions with language understanding and state tracking, including recurrent DQN and Hybrid Code Networks.HCN also permits engineers to incorporate business rules and prior knowledge through software and action templates.
  • Hybrid Learning: Hybrid training can pre-train policies on human dialogues, refine them with teacher-guided imitation learning, and continue learning with reinforcement learning.The cited approach uses DAgger between supervised pre-training and reinforcement-learning continuation.

4.7 Further Remarks

The paper identifies several directions beyond slot-filling dialogue, including richer contexts, multiple participants, weaker learning signals, and evaluation with human conversation data. These settings broaden the research problem while introducing additional complexity and unresolved challenges.

  • Beyond Slot-filling Dialogues: Task-oriented dialogue extends beyond slot filling to information-seeking, navigation, and multimodal interactions involving verbal and nonverbal inputs.The section presents these as examples of more diverse practical dialogues.
  • Complex Interaction Settings: Mixed-initiative dialogues, negotiations, and multi-party conversations introduce more complex turn-taking and motivate game-theoretic perspectives.These scenarios involve more than the usual user-agent interaction.
  • Weaker Learning Signals: Weaker learning signals include preferential input, where a policy receives relative judgments instead of absolute labels or rewards.Such signals are presented as a promising direction for dialogue management at scale.
  • Evaluation: Evaluation remains a major challenge because off-policy evaluation from real human-human conversation corpora is difficult.User simulation is useful, but the paper describes direct human-conversation evaluation as more appealing and robust.
  • Related Areas: Techniques from deep reinforcement learning for text games, including natural-language actions and interpretable policies, may be useful for task-oriented dialogue.Text-game scenarios are predefined by game designers, unlike general conversations.

Fully Data-Driven Conversation Models and Social Bots

Fully data-driven end-to-end conversation models simplify response generation but face persistent challenges in informativeness, repetition, grounding, evaluation, and long-term engagement. Neural extensions address these issues by copying context words, tracking generated content, incorporating external facts, and exploring reinforcement learning.

  • End-to-End Foundations: End-to-end models train response generation directly from data without relying on traditional dialogue-system components.These approaches are commonly developed within the sequence-to-sequence framework.
  • Copying Context: Copy-based extensions improve responses by selecting words from either a fixed vocabulary or the conversational context.CopyNet was shown to improve over RNNs by repeating proper nouns and other input words.
  • Response Quality: Neural response generators often produce bland, deflective responses because likelihood-based training favors frequent, safe outputs.This weakness can undermine long-term user engagement when systems optimize one response at a time rather than a long-term reward.
  • Response Quality: Word repetition remains difficult in dialogue because the task permits source phrases to map to zero or multiple target phrases, unlike relatively one-to-one translation.Machine-translation attention models often do not reduce repetition in dialogue; decoder self-attention was proposed to track generated content and mitigate it.
  • Grounded Conversation: Grounded models augment conversation history with external inputs such as facts, persona, visual context, or emotion to improve real-world relevance.Separating user input from environmental input can support different responses to the same query and improve sample efficiency.
  • Evaluation and Learning: End-to-end dialogue still lacks well-defined success metrics, while trainable evaluation metrics may be vulnerable to being optimized or gamed by generation systems.A learned metric achieved Spearman’s ρ = 0.42 at the sentence level, but the broader evaluation problem remains debated.

Conversational AI in Industry

Industry conversational AI spans QA, task-oriented agents, and chatbots, with commercial systems combining neural and symbolic components across large-scale search, knowledge bases, customer support, and virtual assistants.

  • Question Answering: Commercial QA systems include Web-scale text-QA agents that retrieve documents and use machine reading comprehension to produce direct answers.Bing QA adds an MRC component to the Bing search stack rather than returning only ranked links.
  • Question Answering: Web QA must handle billions of noisy, contradictory, and changing documents while meeting stringent runtime-latency requirements.The MRC component in Bing QA is required to add no more than 10 milliseconds to the serving stack.
  • Question Answering: Bing QA supports conversational queries by reformulating follow-up questions into search-engine-friendly queries through its Conversational Query Understanding module.Turning Bing QA into a full conversational QA agent additionally requires a dialogue manager.
  • Question Answering: Knowledge-base QA systems such as Satori QA combine neural and symbolic methods, while complex-question answering can decompose questions into simpler questions and recompose their answers.This decomposition strategy is used to improve robustness and runtime efficiency at Web scale.
  • Task-Oriented Dialogues: Customer-support agents conduct multi-turn interactions by clarifying users’ problems while navigating knowledge bases toward pre-compiled solutions or human assistance.These systems may accept both text and voice input.
  • Task-Oriented Dialogues: Virtual assistants provide services such as weather information, alarms, and calling-center support through voice, text, or images.Development platforms such as Microsoft’s Task Completion Platform separate task definitions from the overarching dialogue policy using TaskForm.
  • Chatbots: XiaoIce combines dialogue state tracking, policy selection, empathetic computing, neural generation, and retrieval across voice-stream and message-based conversations.Its empathetic computing module models emotion, intent, opinions, and user background to fit responses to the system persona.

Conclusions and Research Trends

The paper presents conversational systems as optimal decision processes and surveys neural approaches across QA, task completion, chitchat, and recommendation. It reports widespread commercial use of hybrid systems while identifying unified modeling, end-to-end operation, and heterogeneous data as continuing challenges.

  • Conclusions: Neural approaches leverage deep learning and reinforcement learning within a unified optimal-decision framework for QA, task completion, chitchat, and recommendation.The paper presents this framework as a way to conceptualize dialogue systems across tasks and domains.
  • Conclusions: Commercial dialogue systems commonly combine symbolic methods with neural models to provide conversational access to services and information.The paper characterizes hybrid approaches as combining the strengths of both method families.
  • Research Trends: QA agents divide into KB-QA systems for conversational knowledge-base access and text-QA systems using neural MRC models for concise direct answers.Text-QA agents are described as increasingly popular for query types where users expect direct answers.
  • Research Trends: Task-oriented research is moving from separately optimized handcrafted or shallow modules toward more holistic deep-learning and reinforcement-learning optimization.The intended direction includes adaptation to changing environments, tasks, domains, and user behaviors.
  • Research Trends: Most state-of-the-art chatbots use data-driven, end-to-end neural response generation, with newer work extending beyond chitchat to recommendation.The paper frames chatbot development within neural machine translation-style generation.
  • Open Challenges: The effectiveness of a unified modeling framework for system development remains unproved despite its value as a design principle.The paper identifies this as an open research direction.
  • Open Challenges: Fully end-to-end dialogue systems remain preliminary, including the challenge of enabling API calls in fully data-driven pipelines.Grounded conversation modeling is presented as a step toward more goal-oriented conversations.
  • Open Challenges: Heterogeneous conversational data remains difficult to exploit because chitchat data is plentiful whereas goal-oriented datasets are typically small.The paper points to multitask learning and data augmentation as research directions.
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