5,003 research outputs found
A Controllable Model of Grounded Response Generation
Current end-to-end neural conversation models inherently lack the flexibility
to impose semantic control in the response generation process, often resulting
in uninteresting responses. Attempts to boost informativeness alone come at the
expense of factual accuracy, as attested by pretrained language models'
propensity to "hallucinate" facts. While this may be mitigated by access to
background knowledge, there is scant guarantee of relevance and informativeness
in generated responses. We propose a framework that we call controllable
grounded response generation (CGRG), in which lexical control phrases are
either provided by a user or automatically extracted by a control phrase
predictor from dialogue context and grounding knowledge. Quantitative and
qualitative results show that, using this framework, a transformer based model
with a novel inductive attention mechanism, trained on a conversation-like
Reddit dataset, outperforms strong generation baselines.Comment: AAAI 202
Linguistic calibration through metacognition: aligning dialogue agent responses with expected correctness
Open-domain dialogue agents have vastly improved, but still confidently
hallucinate knowledge or express doubt when asked straightforward questions. In
this work, we analyze whether state-of-the-art chit-chat models can express
metacognition capabilities through their responses: does a verbalized
expression of doubt (or confidence) match the likelihood that the model's
answer is incorrect (or correct)? We find that these models are poorly
calibrated in this sense, yet we show that the representations within the
models can be used to accurately predict likelihood of correctness. By
incorporating these correctness predictions into the training of a controllable
generation model, we obtain a dialogue agent with greatly improved linguistic
calibration
An Analysis of Mixed Initiative and Collaboration in Information-Seeking Dialogues
The ability to engage in mixed-initiative interaction is one of the core
requirements for a conversational search system. How to achieve this is poorly
understood. We propose a set of unsupervised metrics, termed ConversationShape,
that highlights the role each of the conversation participants plays by
comparing the distribution of vocabulary and utterance types. Using
ConversationShape as a lens, we take a closer look at several conversational
search datasets and compare them with other dialogue datasets to better
understand the types of dialogue interaction they represent, either driven by
the information seeker or the assistant. We discover that deviations from the
ConversationShape of a human-human dialogue of the same type is predictive of
the quality of a human-machine dialogue.Comment: SIGIR 2020 short conference pape
Hierarchical Reinforcement Learning for Open-Domain Dialog
Open-domain dialog generation is a challenging problem; maximum likelihood
training can lead to repetitive outputs, models have difficulty tracking
long-term conversational goals, and training on standard movie or online
datasets may lead to the generation of inappropriate, biased, or offensive
text. Reinforcement Learning (RL) is a powerful framework that could
potentially address these issues, for example by allowing a dialog model to
optimize for reducing toxicity and repetitiveness. However, previous approaches
which apply RL to open-domain dialog generation do so at the word level, making
it difficult for the model to learn proper credit assignment for long-term
conversational rewards. In this paper, we propose a novel approach to
hierarchical reinforcement learning, VHRL, which uses policy gradients to tune
the utterance-level embedding of a variational sequence model. This
hierarchical approach provides greater flexibility for learning long-term,
conversational rewards. We use self-play and RL to optimize for a set of
human-centered conversation metrics, and show that our approach provides
significant improvements -- in terms of both human evaluation and automatic
metrics -- over state-of-the-art dialog models, including Transformers
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