Source-linked AI summary
A Neural Conversational Model
Oriol Vinyals, Quoc Le
TL;DR
Conversational modeling has often depended on narrow domains and handcrafted rules. This paper uses an end-to-end recurrent sequence-to-sequence model that predicts replies from conversation context, finding simple useful conversations and limited reasoning across helpdesk and movie-subtitle data, while consistency remains a problem.
Problem
Previous conversational systems were often narrow in domain and required substantial handcrafted rules or feature engineering.
Method
The model uses recurrent sequence-to-sequence prediction, mapping concatenated conversation context to a reply through end-to-end training.
Results
The model generates simple conversations, sometimes solves IT helpdesk problems, and performs simple common-sense reasoning on noisy movie-subtitle data.
Takeaways & Limitations
A purely data-driven approach without rules can produce proper answers to many types of questions, although realistic conversation may require substantial modification.
Takeaways & Limitations
The model lacks consistent personality, producing inconsistent answers to semantically similar questions, and its objective does not capture longer-term human communication.
Abstract
from arXiv · showhide
Conversational modeling is an important task in natural language understanding and machine intelligence. Although previous approaches exist, they are often restricted to specific domains (e.g., booking an airline ticket) and require hand-crafted rules. In this paper, we present a simple approach for this task which uses the recently proposed sequence to sequence framework. Our model converses by predicting the next sentence given the previous sentence or sentences in a conversation. The strength of our model is that it can be trained end-to-end and thus requires much fewer hand-crafted rules. We find that this straightforward model can generate simple conversations given a large conversational training dataset. Our preliminary results suggest that, despite optimizing the wrong objective function, the model is able to converse well. It is able extract knowledge from both a domain specific dataset, and from a large, noisy, and general domain dataset of movie subtitles. On a domain-specific IT helpdesk dataset, the model can find a solution to a technical problem via conversations. On a noisy open-domain movie transcript dataset, the model can perform simple forms of common sense reasoning. As expected, we also find that the lack of consistency is a common failure mode of our model.
1. Introduction
The paper applies end-to-end sequence-to-sequence modeling to conversational response generation, replacing much handcrafted, domain-specific design with next-sequence prediction. Experiments show useful behavior on both IT helpdesk and noisy movie-subtitle conversations, despite inconsistency limitations.
- The approach requires little feature engineering and domain specificity while addressing mappings between complicated structures.
- Conversational modeling is cast as predicting the next sequence from the preceding sequence or sequences using recurrent networks.
- On IT helpdesk and movie-subtitle datasets, the model sometimes tracks problems, provides useful answers, holds natural conversations, and performs simple common-sense reasoning.
- The recurrent networks obtain better perplexity than the n-gram model and capture important long-range correlations in both datasets.
2. Related Work
The paper builds on sequence-to-sequence neural modeling, recurrent language modeling, and prior dialogue systems. It distinguishes its approach from conventional conversational agents by emphasizing an end-to-end system without domain knowledge.
- Sequence-to-sequence neural networks had been applied to machine translation, parsing, and image captioning before this work.
- Prior neural language-modeling work established recurrent neural networks as effective models for natural language.
- Earlier conversational agents commonly relied on complicated processing pipelines with many stages.
- This work differs by proposing an end-to-end conversational approach that lacks domain knowledge and could rescore candidate responses from other systems.
3. Model
The model uses recurrent sequence-to-sequence prediction to map conversation context to a reply. Its simplicity supports broad task reuse, but next-step training and missing consistency or world-knowledge mechanisms limit dialogue modeling.
- The recurrent model reads an input sequence token by token and predicts the output sequence token by token.Training uses the true output sequence for backpropagation, while inference feeds each predicted token into the next step.
- A two-turn example maps “ABC” to “WXYZ,” with the end-of-sequence hidden state serving as a thought vector for the input sentence.
- For conversations, the input is the concatenated context and the output is the reply.
- The same architecture can support machine translation, question answering, and conversations without major changes.
- Optimizing next-step prediction does not capture the longer-term information exchange of human communication, and the model lacks consistency and general world knowledge.
4. Datasets
Experiments use a closed-domain IT helpdesk troubleshooting dataset and a large, noisy, open-domain movie-subtitle dataset. Their conversation structure, preprocessing, and scale differ substantially.
- The experiments use closed-domain IT helpdesk conversations and open-domain movie transcripts.
- The IT helpdesk threads average 400 words, have clearly signaled turn taking, and include 30M training tokens plus 3M validation tokens.
- OpenSubtitles contains movie-character sentences processed to remove XML tags and obvious non-conversational text.
- Because turn taking is unclear in OpenSubtitles, consecutive sentences are treated as if spoken by different characters and each sentence is used as context and target.
- The OpenSubtitles split contains 62M training sentences and 26M validation sentences, but consecutive sentences may come from the same character.
5. Experiments
The experiments evaluate the recurrent conversational model on IT helpdesk and OpenSubtitles data, with qualitative samples and human comparison against CleverBot. The model shows useful troubleshooting behavior, plausible open-domain responses, and improved perplexity over n-gram baselines, but exhibits inconsistency.
- Human evaluation: The experiments compare the model with a rule-based bot using human evaluations on 200 questions.Judges selected the preferred answer or declared a tie.
- IT Helpdesk Troubleshooting: On IT helpdesk conversations, the model sometimes tracks technical problems and provides useful answers.Samples include VPN, browser, and password troubleshooting interactions.
- IT Helpdesk Troubleshooting: The IT helpdesk recurrent model achieved perplexity 8, compared with 18 for an n-gram model.This result accompanies simulated troubleshooting sessions for common IT problems.
- OpenSubtitles: On OpenSubtitles, the recurrent model achieved validation perplexity 17, compared with 28 for a smoothed 5-gram model.Adding soft attention did not significantly improve training or validation perplexity.
- OpenSubtitles: Open-domain samples show plausible answers, context-sensitive responses, and simple common-sense reasoning, including answers to questions absent from training.The model can remember facts and generalize to new questions without an explicit knowledge-representation component.
- Limitations: A recurring limitation is inconsistency: semantically similar questions can receive different answers because of the model and dataset simplicity.The model also produces simple, short, and sometimes unsatisfying answers.
- Human evaluation: 97 out of 200 questions favored the model, compared with 60 for CleverBot, while 20 were ties and 23 had judge disagreement.A score was recorded when three of four judges agreed.
6. Discussion
The paper shows that a simple seq2seq language model can train a conversational engine and generate basic conversations from noisy, open-domain data. Despite this promise, substantial limitations remain, especially the absence of a coherent personality.
- A simple seq2seq language model can be used to train a conversational engine.
- The model generates simple and basic conversations and extracts knowledge from a noisy, open-domain dataset.
- A purely data-driven approach without rules can produce proper answers to many types of questions.
- The model may require substantial modifications for realistic conversations, while its lack of coherent personality makes passing the Turing test difficult.