| Challenge: | Sentence function is a significant factor to achieve the purpose of the speaker, but has not been touched in large-scale conversation generation. |
| Approach: | They propose a model to generate informative responses with controlled sentence function using a latent variable and a type controller to deal with compatibility. |
| Outcome: | The proposed model outperforms state-of-the-art models and generates responses with controlled sentence function and informative content. |
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Fine-Grained Sentence Functions for Short-Text Conversation (P19-1)
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| Challenge: | Existing research has analyzed various factors indicating the conversational purpose such as emotions, topics, word orders, syntactic patterns and other aspects. |
| Approach: | They propose to annotate a short-text conversation dataset with annotated sentences and train conversation models conditioned on the sentence functions. |
| Outcome: | The proposed model can predict the quality of the returned responses. |
Dialogue Generation on Infrequent Sentence Functions via Structured Meta-Learning (2020.findings-emnlp)
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| Challenge: | Sentence function is an important linguistic feature indicating the communicative purpose of a sentence in a conversation. |
| Approach: | They propose a structured meta-learning approach for dialogue generation on infrequent sentence functions. |
| Outcome: | The proposed approach improves informativeness and relevance of dialogue generation on infrequent sentence functions while preserving knowledge generalization for similar sentence functions. |
Text Generation with Exemplar-based Adaptive Decoding (N19-1)
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| Challenge: | Empirical results show that the proposed model achieves strong performance and outperforms comparable baselines. |
| Approach: | They propose a conditioned text generation model that uses a template-based approach to generate content from input text. |
| Outcome: | The proposed model outperforms baselines on abstractive text summarization and data-to-text generation. |
Long-term Control for Dialogue Generation: Methods and Evaluation (2022.naacl-main)
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| Challenge: | Current approaches for controlling dialogue response generation focus on high-level attributes like style, sentiment, or topic. |
| Approach: | They propose a method that allows for more fine-grained control of dialogue response generation . they propose utterances that encourage the generation of control words in the future . |
| Outcome: | The proposed method outperforms state-of-the-art constrained generation baselines on task-oriented dialogue datasets and shows that it is more fine-grained than previous methods. |
TSDG: Content-aware Neural Response Generation with Two-stage Decoding Process (2020.findings-emnlp)
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| Challenge: | Empirical results show that generative models often use a single decoder to generate a complete response at a stroke. |
| Approach: | They propose a content-aware model with two-stage decoding process to separate content words from function words. |
| Outcome: | The proposed model outperforms competing models in automatic and human evaluation on two datasets. |
Pragmatically Informative Text Generation (N19-1)
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| Challenge: | Existing approaches to pragmatics have been used to improve the informativeness of generated text in grounded language learning problems. |
| Approach: | They propose to use pragmatics to improve the informativeness of conditional text models . they propose to apply pragmatic reasoning to more traditional language generation tasks . |
| Outcome: | The proposed methods improve the performance of strong existing systems for abstractive summarization and generation from structured meaning representations. |
Long Text Generation with Topic-aware Discrete Latent Variable Model (2022.emnlp-main)
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| Challenge: | Recent work focuses on the modeling of discourse relation, resulting in discrete codes learning shallow semantics. |
| Approach: | They propose a topic-aware latent code-guided text generation model that encourages discrete codes to model information about topics. |
| Outcome: | The proposed model generates more topic-relevant and coherent texts. |
Narrative Text Generation with a Latent Discrete Plan (2020.findings-emnlp)
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| Challenge: | Prior work on story generation has focused on generating consistent stories via story outlines using keywords or key phrases. |
| Approach: | They propose a deep latent variable model that first samples a sequence of anchor words, one per sentence in the story, as part of its generative process. |
| Outcome: | The proposed model gets favorable scores when evaluated on perplexity, diversity, and control of story via discrete plan. |
Select and Attend: Towards Controllable Content Selection in Text Generation (D19-1)
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| Challenge: | Recent neural network models conflate content selection and surface realization into a black-box architecture, resulting in content to be described in text cannot be explicitly controlled. |
| Approach: | They propose to decouple content selection from the decoder to allow finer-grained control over the generation. |
| Outcome: | The proposed model can be trained end-to-end without human annotations and achieves promising results in data-totext and headline generation tasks. |
Adapting a Language Model for Controlled Affective Text Generation (2020.coling-main)
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| Challenge: | Existing models for affective text generation fail to capture emotional aspects of conversations without explicit affective information. |
| Approach: | They propose to incorporate emotion as prior for the probabilistic state-of-the-art text generation model such as GPT-2 and incorporate emotion into the model to ensure grammatical correctness. |
| Outcome: | The proposed model outperforms existing models in all intensities and is robust to human evaluations. |