Papers with open-domain systems
Deep Learning for Conversational AI (N18-6)
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| Challenge: | Spoken Dialogue Systems (SDS) have great commercial potential . the advent of deep learning has led to significant advances in this area of NLP research . |
| Approach: | This tutorial will introduce researchers to the pipeline framework for modelling goal-oriented dialogue systems. |
| Outcome: | This tutorial will familiarise researchers with the latest advances in spoken dialogue systems . the aim of the course is to encourage dialogue research in the NLP community . |
When Do Decompositions Help for Machine Reading? (2023.emnlp-main)
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| Challenge: | Existing work on decompositions of complex questions has focused on multi-step reasoning . but, in machine reading, it is unclear when decomposing is helpful . |
| Approach: | They conduct experiments on decompositions in machine reading to unify recent work . they find that decomposing complex questions can be helpful in zero or limited-data settings . |
| Outcome: | The proposed model can learn decompositions implicitly even with limited data, the study shows . the results are consistent with previous work on decomposing complex questions . |
Injecting Salesperson’s Dialogue Strategies in Large Language Models with Chain-of-Thought Reasoning (2024.findings-acl)
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| Challenge: | Recent research in dialogue systems focuses on task-oriented (TOD) and open-domain (chit-chat) dialogues. |
| Approach: | They propose to use chit-chat to simulate task-oriented dialogues to train sales agents. |
| Outcome: | The proposed model improves coherence and reduces aggression, improving model learning for sales-customer interactions. |
How Much Knowledge Can You Pack Into the Parameters of a Language Model? (2020.emnlp-main)
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| Challenge: | In this paper, we show that large neural language models trained on unstructured text can attain competitive results on open-domain question answering benchmarks without access to external knowledge. |
| Approach: | They propose to fine-tune pre-trained neural language models to answer questions without external knowledge . they show that this approach scales with model size and performs competitively . |
| Outcome: | The proposed approach scales with model size and performs competitively with open-domain systems that explicitly retrieve answers from an external knowledge source when answering questions. |