Adaptive Bridge between Training and Inference for Dialogue Generation (2021.emnlp-main)
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| Challenge: | Experimental results show that our model can achieve a significant improvement in terms of metric-based evaluation and human evaluation compared with the state-of-the-art exposure bias approaches. |
| Approach: | They propose a novel adaptive switching mechanism which automatically transits between ground-truth learning and generated learning regarding the word-level matching score. |
| Outcome: | The proposed model improves on Chinese and English reddit datasets compared with state-of-the-art models on the word-level matching score. |
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Chenxu Yang, Zheng Lin, Lanrui Wang, Chong Tian, Liang Pang, Jiangnan Li, Qirong Ho, Yanan Cao, Weiping Wang
| Challenge: | Existing knowledge-grounded dialogue generation models struggle with dull and repetitive outputs, a problem commonly termed as text degeneration. |
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Bridging the Gap between Training and Inference for Neural Machine Translation (P19-1)
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| Challenge: | Neural Machine Translation generates target words sequentially while at inference it has to generate the entire sequence from scratch. |
| Approach: | They propose to use ground truth and inference to generate target words sequentially while at inference it has to generate the entire sequence from scratch. |
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Why Exposure Bias Matters: An Imitation Learning Perspective of Error Accumulation in Language Generation (2022.findings-acl)
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| Challenge: | Current language generation models suffer from issues such as repetition, incoherence, and hallucinations . |
| Approach: | They propose to analyze exposure bias from an imitation learning perspective and prove it is a problem . they show that exposure bias leads to an accumulation of errors during generation . |
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Generalization in Generation: A closer look at Exposure Bias (D19-56)
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| Challenge: | Autoregressive generative models are often criticized for using ground-truth contexts at training time but generated ones at test time. |
| Approach: | They propose that generalization is the underlying property to address and propose unconditional generation as its fundamental benchmark. |
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Knowledge-Grounded Dialogue Generation with Pre-trained Language Models (2020.emnlp-main)
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| Challenge: | Empirical results indicate that pre-trained language models can significantly outperform state-of-the-art methods in both automatic evaluation and human judgment. |
| Approach: | They propose to equip a pre-trained language model with a knowledge selection module to generate knowledge-grounded dialogues. |
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Learning to Generate Equitable Text in Dialogue from Biased Training Data (2023.acl-long)
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| Challenge: | Absence of equitable and inclusive principles can hinder the formation of common ground, which in turn negatively impacts the overall performance of the system. |
| Approach: | They propose to use theories of computational learning to study equitable text generation in dialogues using augmented data to prove formal definitions of equity in text generation and formal connections between human-likeness and learning equity. |
| Outcome: | The proposed model predicts relative-performance of multiple algorithms in generating equitable text as measured by human and automated evaluation. |
Avoiding the Hypothesis-Only Bias in Natural Language Inference via Ensemble Adversarial Training (2020.emnlp-main)
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| Challenge: | Neural models pick up on annotation artefacts and spurious correlations, resulting in learning sentences that suffer from the same biases. |
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DialogueCSE: Dialogue-based Contrastive Learning of Sentence Embeddings (2021.emnlp-main)
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| Challenge: | Conventional approaches to learning sentence embeddings from dialogues employ the siamese-network for this task, but such architecture yields a large gap between training and evaluating. |
| Approach: | They propose a dialogue-based contrastive learning approach to learn sentence embeddings from dialogues using a siamese-network. |
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Mitigating Gender Bias for Neural Dialogue Generation with Adversarial Learning (2020.emnlp-main)
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| Challenge: | Recent research shows that dialogue systems trained on human conversation data are biased and can produce responses that reflect people’s gender prejudice. |
| Approach: | They propose a novel adversarial learning framework Debiased-Chat to train dialogue models free from gender bias while keeping their performance. |
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Pretrained Language Models for Dialogue Generation with Multiple Input Sources (2020.findings-emnlp)
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| Challenge: | Large-scale pretrained language models have achieved outstanding performance on natural language understanding tasks. |
| Approach: | They propose to fuse attention information from multiple input sources to achieve better relevance with dialogue history than simple fusion baselines. |
| Outcome: | The proposed models deliver higher relevance with dialogue history than baselines. |