Linguistically-Informed Specificity and Semantic Plausibility for Dialogue Generation

By Wei-Jen Ko, Greg Durrett, Junyi Jessy Li. 2019

32 citations recorded by the source corpus.

Sequence-to-sequence models for open-domain dialogue generation tend to favor generic, uninformative responses. Past work has focused on word frequency-based approaches to improving specificity, such as penalizing responses with only common words. In this work, we examine whether specificity is solely a frequency-related notion and find that more linguistically-driven specificity measures are better suited to improving response informativeness. However, we find that forcing a sequence-to-sequence model to be more specific can expose a host of other problems in the responses, including flawed discourse and implausible semantics. We rerank our model’s outputs using externally-trained classifiers targeting each of these identified factors. Experiments show that our final model using linguistically motivated specificity and plausibility reranking improves the informativeness, reasonableness, and grammatically of responses.

Subfields: Artificial Intelligence, Natural Language Processing

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Citations in the corpus

Towards a Human-like Open-Domain Chatbot (2020, methodology): “These added complexities sometimes lead to implausible responses as analyzed by Ko et al. (2019).”