Papers with automatic measures
Using Question Answering Rewards to Improve Abstractive Summarization (2021.findings-emnlp)
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| Challenge: | Neural abstractive summarization models have seen improvements in recent years, but they still suffer from multiple drawbacks. |
| Approach: | They propose a general framework to train abstractive summarization models to alleviate these issues by question-answering based rewards. |
| Outcome: | The proposed framework is preferred over general abstractive summarization models. |
Comparing Automatic and Human Evaluation of Local Explanations for Text Classification (N18-1)
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| Challenge: | Text classification models are becoming increasingly complex and opaque, however for many applications it is essential that the models are interpretable. |
| Approach: | They propose to use automatic word deletion to generate local explanations for a text classification model by crowdsourcing the evaluation using a crowdsourced experiment. |
| Outcome: | The proposed evaluations of local explanations using crowdsourcing and automatic measures correlate with the results. |
Summary Grounded Conversation Generation (2021.findings-acl)
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| Challenge: | Existing datasets for conversation summarization are small due to the lack of large-scale datasets. |
| Approach: | They propose three approaches to generate summary grounded conversations, and evaluate the generated conversations using automatic measures and human judgements. |
| Outcome: | The proposed models can generate entire conversations with only a summary of a conversation as the input. |
Improving Factual Consistency for Knowledge-Grounded Dialogue Systems via Knowledge Enhancement and Alignment (2023.findings-emnlp)
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Boyang Xue, Weichao Wang, Hongru Wang, Fei Mi, Rui Wang, Yasheng Wang, Lifeng Shang, Xin Jiang, Qun Liu, Kam-Fai Wong
| Challenge: | Experimental results show that pretrained language models generate inconsistent factual knowledge in many conversational tasks. |
| Approach: | They propose a method which explicitly introduces extended feedforward networks (FFNs) in Transformers to enhance factual knowledge expressions given the specific patterns of knowledge-grounded dialogue inputs. |
| Outcome: | The proposed methods improve the factual expression capability of feedforward networks (FFNs) in knowledge-grounded dialogue systems by knowledge enhancement and alignment respectively. |