Papers with open-domain conversational agents
Fluent Response Generation for Conversational Question Answering (2020.acl-main)
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| Challenge: | Question answering (QA) is an important aspect of open-domain conversational agents, garnering specific research focus in the conversational QA subtask. |
| Approach: | They propose a method for situating QA responses within a SEQ2SEQ NLG approach to generate fluent grammatical answer responses while maintaining correctness. |
| Outcome: | The proposed model outperforms baseline CoQA and QuAC models in generating conversational responses. |
Multi-Modal Open-Domain Dialogue (2021.emnlp-main)
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| Challenge: | Recent work in open-domain conversational agents has demonstrated that significant improvements in humanness and user preference can be achieved via massive scaling in both pre-training data and model size. |
| Approach: | They combine open-domain dialogue agents with vision models to investigate human preferences and humanness. |
| Outcome: | The proposed model outperforms existing models in multi-modal dialogue while performing as well as its predecessor (text-only) BlenderBot. |
MRF-Chat: Improving Dialogue with Markov Random Fields (2021.emnlp-main)
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| Challenge: | Existing approaches to deep learning for open-domain dialogue include training end-to-end models to learn various conversational features like emotional content of response, symbolic transitions of dialogue contexts and persona of the agent and the user, among others. |
| Approach: | They propose a probabilistic approach using Markov Random Fields to augment existing deep-learning methods for improved next utterance prediction. |
| Outcome: | The proposed approach significantly improves the performance of existing state-of-the-art retrieval models for open-domain conversational agents. |
Better Conversations by Modeling, Filtering, and Optimizing for Coherence and Diversity (D18-1)
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| Challenge: | Existing encoder-decoder models for open domain dialogue generate generic, uninformative, and non-coherent responses. |
| Approach: | They propose to introduce a measure of coherence as the GloVe embedding similarity between dialogue context and generated response to improve output diversity. |
| Outcome: | The proposed model improves on the OpenSubtitles corpus in terms of BLEU score and diversity metrics. |