Papers with conversational response generation
MEMD: A Diversity-Promoting Learning Framework for Short-Text Conversation (C18-1)
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| Challenge: | Neural encoder-decoder models tend to generate meaningless and generic responses regardless of what the input text is. |
| Approach: | They propose an easy-to-extend learning framework based on latent vectors to provide training guidance without resorting to extra data or complicating network’s inner structure. |
| Outcome: | The proposed framework improves the quality of generated responses according to automatic metrics and human evaluations, yielding more diverse and smooth replies. |
Language Model as an Annotator: Exploring DialoGPT for Dialogue Summarization (2021.acl-long)
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| Challenge: | Existing dialogue summarization systems encode text with a number of general semantic features, but these are often not available in open-domain tools. |
| Approach: | They propose to use DialoGPT to label three types of features on two datasets . they propose to employ pre-trained and non-pre-tried models as dialogue annotators . |
| Outcome: | The proposed method improves on two dialogue summarization datasets and achieves state-of-the-art performance. |
RedditBias: A Real-World Resource for Bias Evaluation and Debiasing of Conversational Language Models (2021.acl-long)
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| Challenge: | Recent work has focused on measuring and mitigating bias in pretrained language models. |
| Approach: | They propose a dataset that measures and mitigates bias across gender,race, religion, and queerness . they compare REDDITBIAS to a widely used conversational DialoGPT model . |
| Outcome: | The proposed framework measures and mitigates bias across gender,race, religion, and queerness dimensions. |
PALM: Pre-training an Autoencoding&Autoregressive Language Model for Context-conditioned Generation (2020.emnlp-main)
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| Challenge: | Existing techniques for natural language understanding and generation use autoencoding and/or autoregressive objectives to train models. |
| Approach: | They propose a self-supervised pre-training scheme that pre-trains an autoencoding and autoregressive language model on a large unlabeled corpus for generating new text conditioned on context. |
| Outcome: | The proposed scheme achieves state-of-the-art results on a variety of language generation benchmarks covering generative question answering, abstractive summarization and conversational response generation. |