Corpus ID: 237396261

Probing Commonsense Explanation in Dialogue Response Generation

@inproceedings{Zhou2021ProbingCE,
  title={Probing Commonsense Explanation in Dialogue Response Generation},
  author={Pei Zhou and Pegah Jandaghi and Bill Yuchen Lin and Justin Cho and Jay Pujara and Xiang Ren},
  year={2021}
}
  • Pei Zhou, Pegah Jandaghi, +3 authors Xiang Ren
  • Published 2021
  • Computer Science
Humans use commonsense reasoning (CSR) implicitly to produce natural and coherent responses in conversations. Aiming to close the gap between current response generation (RG) models and human communication abilities, we want to understand why RG models respond as they do by probing RG model’s understanding of commonsense reasoning that elicits proper responses. We formalize the problem by framing commonsense as a latent variable in the RG task and using explanations for responses as textual… Expand

References

SHOWING 1-10 OF 45 REFERENCES
Commonsense-Focused Dialogues for Response Generation: An Empirical Study
Smooth and effective communication requires the ability to perform latent or explicit commonsense inference. Prior commonsense reasoning benchmarks (such as SocialIQA and CommonsenseQA) mainly focusExpand
Commonsense Knowledge Aware Conversation Generation with Graph Attention
TLDR
This is the first attempt that uses large-scale commonsense knowledge in conversation generation, and unlike existing models that use knowledge triples (entities) separately and independently, this model treats each knowledge graph as a whole, which encodes more structured, connected semantic information in the graphs. Expand
Wizard of Wikipedia: Knowledge-Powered Conversational agents
TLDR
The best performing dialogue models are able to conduct knowledgeable discussions on open-domain topics as evaluated by automatic metrics and human evaluations, while a new benchmark allows for measuring further improvements in this important research direction. Expand
Grounded Conversation Generation as Guided Traverses in Commonsense Knowledge Graphs
TLDR
A new conversation generation model, ConceptFlow, which leverages commonsense knowledge graphs to explicitly model conversation flows, demonstrating ConceptFlow’s effectiveness over previous knowledge-aware conversation models and GPT-2 based models while using 70% fewer parameters, confirming the advantage of explicit modeling conversation structures. Expand
Social IQA: Commonsense Reasoning about Social Interactions
TLDR
Social IQa is introduced, the first largescale benchmark for commonsense reasoning about social situations, using a new framework that mitigates stylistic artifacts in incorrect answers by asking workers to provide the right answer to a different but related question. Expand
MuTual: A Dataset for Multi-Turn Dialogue Reasoning
TLDR
MuTual is introduced, a novel dataset for Multi-Turn dialogue Reasoning, consisting of 8,860 manually annotated dialogues based on Chinese student English listening comprehension exams, which shows that there is ample room for improving reasoning ability. Expand
Towards Empathetic Open-domain Conversation Models: A New Benchmark and Dataset
TLDR
This work proposes a new benchmark for empathetic dialogue generation and EmpatheticDialogues, a novel dataset of 25k conversations grounded in emotional situations, and presents empirical comparisons of dialogue model adaptations forEmpathetic responding, leveraging existing models or datasets without requiring lengthy re-training of the full model. Expand
DIALOGPT : Large-Scale Generative Pre-training for Conversational Response Generation
TLDR
It is shown that conversational systems that leverage DialoGPT generate more relevant, contentful and context-consistent responses than strong baseline systems. Expand
“Nice Try, Kiddo”: Investigating Ad Hominems in Dialogue Responses
TLDR
Responses to Twitter topics about marginalized communities versus other topics are compared, and a constrained decoding technique that uses salient n-gram similarity as a soft constraint for top-k sampling to reduce the amount of ad hominems generated is proposed. Expand
Commonsense Reasoning for Natural Language Processing
TLDR
This tutorial organizes this tutorial to provide researchers with the critical foundations and recent advances in commonsense representation and reasoning, in the hopes of casting a brighter light on this promising area of future research. Expand
...
1
2
3
4
5
...