40,671 research outputs found
Personalizing Dialogue Agents via Meta-Learning
Existing personalized dialogue models use human designed persona descriptions
to improve dialogue consistency. Collecting such descriptions from existing
dialogues is expensive and requires hand-crafted feature designs. In this
paper, we propose to extend Model-Agnostic Meta-Learning (MAML)(Finn et al.,
2017) to personalized dialogue learning without using any persona descriptions.
Our model learns to quickly adapt to new personas by leveraging only a few
dialogue samples collected from the same user, which is fundamentally different
from conditioning the response on the persona descriptions. Empirical results
on Persona-chat dataset (Zhang et al., 2018) indicate that our solution
outperforms non-meta-learning baselines using automatic evaluation metrics, and
in terms of human-evaluated fluency and consistency.Comment: Accepted in ACL 2019. Zhaojiang Lin* and Andrea Madotto* contributed
equally to this wor
A Knowledge-Grounded Multimodal Search-Based Conversational Agent
Multimodal search-based dialogue is a challenging new task: It extends
visually grounded question answering systems into multi-turn conversations with
access to an external database. We address this new challenge by learning a
neural response generation system from the recently released Multimodal
Dialogue (MMD) dataset (Saha et al., 2017). We introduce a knowledge-grounded
multimodal conversational model where an encoded knowledge base (KB)
representation is appended to the decoder input. Our model substantially
outperforms strong baselines in terms of text-based similarity measures (over 9
BLEU points, 3 of which are solely due to the use of additional information
from the KB
Debbie, the Debate Bot of the Future
Chatbots are a rapidly expanding application of dialogue systems with
companies switching to bot services for customer support, and new applications
for users interested in casual conversation. One style of casual conversation
is argument, many people love nothing more than a good argument. Moreover,
there are a number of existing corpora of argumentative dialogues, annotated
for agreement and disagreement, stance, sarcasm and argument quality. This
paper introduces Debbie, a novel arguing bot, that selects arguments from
conversational corpora, and aims to use them appropriately in context. We
present an initial working prototype of Debbie, with some preliminary
evaluation and describe future work.Comment: IWSDS 201
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