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Exploring Controllable Text Generation Techniques

Shrimai Prabhumoye, Alan W Black, Ruslan Salakhutdinov

arXiv:2005.01822v2cs.CL

TL;DR

Prior controllable-text-generation work addresses specific tasks and contexts without a unifying theme. The paper organizes generation into five modules and surveys techniques for modulating them, offering theoretical understanding and qualitative analysis that the authors say paves the way for combined-module architectures.

  • Problem

    Prior controllable text generation work addresses specific tasks and contexts, with no unifying theme connecting the field.

    Source: §1 · p. 1

  • Method

    The paper proposes a five-module schema for the generation pipeline and surveys techniques for controlling and updating each module.

    Source: §1 · pp. 1–2

  • Results

    The paper provides theoretical understanding and qualitative analysis of techniques for modulating the five modules.

    Source: §8 · pp. 9–10

  • Takeaways & Limitations

    The authors say this understanding paves the way for new architectures combining the described modules.

    Source: §8 · p. 10

    For the decomposed-representation techniques discussed, effectiveness remains unproven in style transfer and abstractive summarization, and reported models did not beat task state of the art.

    Source: §2.3 · p. 4

Abstract

from arXiv · show

Neural controllable text generation is an important area gaining attention due to its plethora of applications. Although there is a large body of prior work in controllable text generation, there is no unifying theme. In this work, we provide a new schema of the pipeline of the generation process by classifying it into five modules. The control of attributes in the generation process requires modification of these modules. We present an overview of different techniques used to perform the modulation of these modules. We also provide an analysis on the advantages and disadvantages of these techniques. We further pave ways to develop new architectures based on the combination of the modules described in this paper.

1 Introduction

The paper addresses the lack of a unifying framework for controllable text generation, a task used to control attributes such as style, demographics, content, and information order. It proposes a five-module schema for organizing prior techniques and describing how they modulate generation.

1 Introduction

  • Controllable text generation produces natural sentences with controllable attributes, including style, writer demographics, content, and information ordering.Applications described include dialogue, story generation, email modulation, and report generation.Source: §1 · p. 1
  • The paper proposes a five-module schema that connects prior work and organizes techniques for controlling and updating components of the generation process.The modules cover external input, sequential input, generator operations, output, and training objective.Source: §1 · pp. 1–2
  • The schema can describe controllable generation across algorithmic paradigms, including sequence-to-sequence, adversarial, reinforcement-learning, and non-autoregressive methods.The paper focuses on applying the schema to autoregressive models.Source: §1 · p. 2
  • Unlike prior generation frameworks that divide processes into subtasks or focus on toolkits and implementation details, this work describes the required modules of neural controllable generation.It details generation inputs, outputs, and operations without prescribing specific architectures.Source: §1 · p. 2

2 External Input

External-input control modifies the generator's initialization, with approaches ranging from combining control and encoder representations to stochastic changes, decomposition, and external feedback.

2 External Input

  • The section treats changes to the generator's initialization h0 as external-input control; standard generation sets h0 equal to the encoder representation he.These methods modify the generator's initial state.Source: §2 · p. 3

2.1 Arithmetic or Linear Transform

  • Concatenation gives s a strong signal, while addition can blur it; small s may be overshadowed unless dimensions are comparable, making linear transforms an alternative.For Hoang et al. (2016), the linear transform performed better than the other two techniques; comparable dimensions also increase model size and cost.Source: §2.1 · p. 3

2.2 Stochastic Changes

  • Stochastic initialization uses latent variables and requires KL-Divergence training; it suits attributes representable as latent variables, but is difficult for content-grounded generation.Examples of latent attributes include style, topics, and strategies; content-grounded tasks may require specific information, keywords, or entities as control.Source: §2.2 · p. 3

2.3 Decompose

  • Decomposition separates encoder representations into attribute subspaces, such as structure and meaning or form and meaning, yielding interpretable representations but not established task-leading performance.Its effectiveness remains unproven for style transfer and abstractive summarization, and learning good decomposed vectors is especially hard without supervision.Source: §2.3 · pp. 3–4
  • Decomposition works best when the input representation contains the control signal; it is unlikely to fit tasks where required content is provided externally.Content-grounded generation may supply information to generate separately rather than include it in the input representation.Source: §2.3 · p. 4

2.4 External Feedback

  • External feedback uses losses on the initialization or decomposed representations to encourage desired style information and discourage unwanted style signals.A discriminator providing feedback must be jointly trained with the generator; feedback can also help ensure decomposed subspaces represent the intended attributes.Source: §2.4 · p. 4

3 Sequential Input

Sequential-input methods modify the decoder input at each generation step, often by adding control information. Reported results vary by task, and these approaches are not conclusively better than changing external input.

3 Sequential Input

  • Sequential input x_t is the decoder's word embedding at each time step, and the section surveys techniques for manipulating it.The paper identifies this as position (2) in the generation pipeline.Source: §3 · p. 4

3.1 Arithmetic or Linear Transform

  • Per-step concatenation was ineffective for definition modeling, while for stylistically varied language from meaning representations it beat encoder conditioning on BLEU.These task-specific results concern concatenating control information at each decoder step.Source: §3.1 · pp. 4–5
  • For personality-conditioned stories, subtracting the average story representation and adding personality representation outperformed concatenation, but neither approach was conclusively better than external-input changes.The comparison concerns the sequential-input techniques described for story generation.Source: §3.1 · p. 5

4 Generator Operations

Generator Operations is the module that applies a consistent computation to the external and sequential inputs to produce each output. The section surveys recurrent, Transformer, and pretrained-model approaches and how their computations or inputs accommodate control attributes.

4 Generator Operations

  • Generator Operations applies computation G to external input h0 and sequential input xt at each step to produce output ot.Source: §4 · p. 5

4.1 Recurrent Neural Networks

  • RNNs reuse operations across sequence elements and retain context through recurrence, but empirically look back only a few steps; LSTMs use memory cells and gates for longer-term dependencies.Source: §4.1 · p. 5
  • Control can enter recurrent computations through added gates or inputs: factored LSTM style-specific S captures style features, while dialogue-act information can be supplied through another LSTM cell.Source: §4.1 · pp. 5–6
  • A modified GRU for recipe generation adds goal and agenda-item inputs, with select gates controlling generation according to the goal and recipe items.Source: §4.1 · pp. 5–6

4.2 Transformer

  • Transformers use stacked self-attention and feed-forward layers, with decoder attention over encoder outputs; their sequence computations can be parallelized, but RNN control modifications have not been explored with them.Source: §4.2 · p. 6

4.3 Pre-trained models

  • Pretrained conditional language models bring learned fluency and grammaticality, but some control techniques require architectural changes or costly retraining; BART and T5 are described as adaptable to text generation.Source: §4.3 · p. 6

5 Output

The Output module modifies the generator output at each time step before vocabulary projection. The section reviews attention, external feedback, and arithmetic transformations as ways to control this stage.

5 Output

  • The Output module modulates generator output ot at each time step before projecting it to the vocabulary to predict a token.Source: §5 · p. 6

5.1 Attention

  • Attention forms a source-context vector from the current hidden state to aid token prediction; global attention considers all source states but is expensive for long sequences, while local attention uses a window.Source: §5.1 · pp. 6–7

5.2 External Feedback

  • External feedback can alter the output latent space through adversarial losses or rewards for style, semantics, and fluency; discriminator-based adversarial loss requires joint training with the generator.Source: §5.2 · p. 7

5.3 Arithmetic or Linear Transform

  • Output transformations add the control vector, concatenate it with ot, or use a control-dependent perceptron before vocabulary projection.Source: §5.3 · p. 7

6 Training Objective

The Training Objective module uses loss functions that can be adjusted to encourage desired attributes in generated text. The section covers general generation objectives, KL divergence, classifier losses, and task-specific losses.

6 Training Objective

  • The Training Objective module compares predicted tokens with references using a loss that can be adjusted to encourage desired control attributes.Source: §6 · p. 7

6.1 General Loss Objective

  • Cross entropy compares generated tokens with reference tokens, while unlikelihood loss penalizes repeated tokens or n-grams and can augment maximum-likelihood training.Source: §6.1 · pp. 7–8
  • Post-hoc decoding strategies include stochastic sampling and beam-search variants; diversity-promoting objectives such as MMI aim to reduce generic dialogue responses.Source: §6.1 · p. 8

6.2 KL Divergence

  • KL divergence is combined with ELBO to approximately maximize marginal data likelihood and approximate the posterior distribution of control variables in latent z-space.Source: §6.2 · p. 8

6.3 Classifier Loss

  • Classifier losses assess whether generated tokens meet a target attribute, but style-classification designs may lose accuracy as the number of styles grows or require a separate loss per style.Source: §6.3 · p. 8

6.4 Task Specific Loss

  • Task-specific losses encode desired strategy alignment, penalize repeated source attention, or regularize sentence-level compression and coverage for document summarization.Source: §6.4 · p. 9

7 Discussion

Classifier-based attribute control faces a differentiability challenge when generated tokens are selected with argmax. The discussion also describes gradient-estimation alternatives and notes that finding effective combinations of control modules remains open.

7 Discussion

  • Argmax-generated tokens are not differentiable, making it challenging to pass them effectively to the classifier for attribute-loss calculation.Source: §7 · p. 9
  • REINFORCE can estimate rewards through sampled next tokens but has unstable, high-variance gradients; Gumbel-softmax approximates a multinomial distribution parameterized by softmax.Source: §7 · p. 9
  • Prior systems combine techniques across modules, but determining the best combination through empirical evaluation remains an open challenge.Source: §7 · p. 9

8 Conclusion and Future Work

The paper organizes prior work in controllable text generation with a five-module schema and describes techniques for modulating those modules. It offers theoretical understanding and qualitative analysis, with module combinations proposed as a basis for new architectures.

8 Conclusion and Future Work

  • The proposed five-module schema organizes prior work in controllable text generation, with each module playing an important role in generation.The paper details techniques for modulating the modules to control text generation.Source: §8 · p. 9
  • The paper provides theoretical understanding and qualitative analysis of the techniques, and suggests combining modules as a basis for new architectures.Source: §8 · p. 10
  • Future work will empirically compare the techniques to assess their usefulness and strength.Source: §8 · p. 10

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