1,269 research outputs found
Generating Persona Consistent Dialogues by Exploiting Natural Language Inference
Consistency is one of the major challenges faced by dialogue agents. A
human-like dialogue agent should not only respond naturally, but also maintain
a consistent persona. In this paper, we exploit the advantages of natural
language inference (NLI) technique to address the issue of generating persona
consistent dialogues. Different from existing work that re-ranks the retrieved
responses through an NLI model, we cast the task as a reinforcement learning
problem and propose to exploit the NLI signals from response-persona pairs as
rewards for the process of dialogue generation. Specifically, our generator
employs an attention-based encoder-decoder to generate persona-based responses.
Our evaluator consists of two components: an adversarially trained naturalness
module and an NLI based consistency module. Moreover, we use another
well-performed NLI model in the evaluation of persona-consistency. Experimental
results on both human and automatic metrics, including the model-based
consistency evaluation, demonstrate that the proposed approach outperforms
strong generative baselines, especially in the persona-consistency of generated
responses.Comment: AAAI20. Update code link
A Pre-training Based Personalized Dialogue Generation Model with Persona-sparse Data
Endowing dialogue systems with personas is essential to deliver more
human-like conversations. However, this problem is still far from well explored
due to the difficulties of both embodying personalities in natural languages
and the persona sparsity issue observed in most dialogue corpora. This paper
proposes a pre-training based personalized dialogue model that can generate
coherent responses using persona-sparse dialogue data. In this method, a
pre-trained language model is used to initialize an encoder and decoder, and
personal attribute embeddings are devised to model richer dialogue contexts by
encoding speakers' personas together with dialogue histories. Further, to
incorporate the target persona in the decoding process and to balance its
contribution, an attention routing structure is devised in the decoder to merge
features extracted from the target persona and dialogue contexts using
dynamically predicted weights. Our model can utilize persona-sparse dialogues
in a unified manner during the training process, and can also control the
amount of persona-related features to exhibit during the inference process.
Both automatic and manual evaluation demonstrates that the proposed model
outperforms state-of-the-art methods for generating more coherent and persona
consistent responses with persona-sparse data.Comment: Long paper accepted at AAAI 202
Profile Consistency Identification for Open-domain Dialogue Agents
Maintaining a consistent attribute profile is crucial for dialogue agents to
naturally converse with humans. Existing studies on improving attribute
consistency mainly explored how to incorporate attribute information in the
responses, but few efforts have been made to identify the consistency relations
between response and attribute profile. To facilitate the study of profile
consistency identification, we create a large-scale human-annotated dataset
with over 110K single-turn conversations and their key-value attribute
profiles. Explicit relation between response and profile is manually labeled.
We also propose a key-value structure information enriched BERT model to
identify the profile consistency, and it gained improvements over strong
baselines. Further evaluations on downstream tasks demonstrate that the profile
consistency identification model is conducive for improving dialogue
consistency.Comment: EMNLP2
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