12 research outputs found
Towards Empathetic Dialogue Generation over Multi-type Knowledge
Enabling the machines with empathetic abilities to provide context-consistent
responses is crucial on both semantic and emotional levels. The task of
empathetic dialogue generation is proposed to address this problem. However,
lacking external knowledge makes it difficult to perceive implicit emotions
from limited dialogue history. To address the above challenges, we propose to
leverage multi-type knowledge, i.e, the commonsense knowledge and emotional
lexicon, to explicitly understand and express emotions in empathetic dialogue
generation. We first enrich the dialogue history by jointly interacting with
two-type knowledge and construct an emotional context graph. Then we introduce
a multi-type knowledge-aware context encoder to learn emotional context
representations and distill emotional signals, which are the prerequisites to
predicate emotions expressed in responses. Finally, we propose an emotional
cross-attention mechanism to exploit the emotional dependencies between the
emotional context graph and the target empathetic response. Conducted on a
benchmark dataset, extensive experimental results show that our proposed
framework outperforms state-of-the-art baselines in terms of automatic metrics
and human evaluations.Comment: arXiv admin note: text overlap with arXiv:1911.0869
Abstractive Opinion Tagging
In e-commerce, opinion tags refer to a ranked list of tags provided by the
e-commerce platform that reflect characteristics of reviews of an item. To
assist consumers to quickly grasp a large number of reviews about an item,
opinion tags are increasingly being applied by e-commerce platforms. Current
mechanisms for generating opinion tags rely on either manual labelling or
heuristic methods, which is time-consuming and ineffective. In this paper, we
propose the abstractive opinion tagging task, where systems have to
automatically generate a ranked list of opinion tags that are based on, but
need not occur in, a given set of user-generated reviews.
The abstractive opinion tagging task comes with three main challenges: (1)
the noisy nature of reviews; (2) the formal nature of opinion tags vs. the
colloquial language usage in reviews; and (3) the need to distinguish between
different items with very similar aspects. To address these challenges, we
propose an abstractive opinion tagging framework, named AOT-Net, to generate a
ranked list of opinion tags given a large number of reviews. First, a
sentence-level salience estimation component estimates each review's salience
score. Next, a review clustering and ranking component ranks reviews in two
steps: first, reviews are grouped into clusters and ranked by cluster size;
then, reviews within each cluster are ranked by their distance to the cluster
center. Finally, given the ranked reviews, a rank-aware opinion tagging
component incorporates an alignment feature and alignment loss to generate a
ranked list of opinion tags. To facilitate the study of this task, we create
and release a large-scale dataset, called eComTag, crawled from real-world
e-commerce websites. Extensive experiments conducted on the eComTag dataset
verify the effectiveness of the proposed AOT-Net in terms of various evaluation
metrics.Comment: Accepted by WSDM 202
COSPLAY: Concept Set Guided Personalized Dialogue Generation Across Both Party Personas
Maintaining a consistent persona is essential for building a human-like
conversational model. However, the lack of attention to the partner makes the
model more egocentric: they tend to show their persona by all means such as
twisting the topic stiffly, pulling the conversation to their own interests
regardless, and rambling their persona with little curiosity to the partner. In
this work, we propose COSPLAY(COncept Set guided PersonaLized dialogue
generation Across both partY personas) that considers both parties as a "team":
expressing self-persona while keeping curiosity toward the partner, leading
responses around mutual personas, and finding the common ground. Specifically,
we first represent self-persona, partner persona and mutual dialogue all in the
concept sets. Then, we propose the Concept Set framework with a suite of
knowledge-enhanced operations to process them such as set algebras, set
expansion, and set distance. Based on these operations as medium, we train the
model by utilizing 1) concepts of both party personas, 2) concept relationship
between them, and 3) their relationship to the future dialogue. Extensive
experiments on a large public dataset, Persona-Chat, demonstrate that our model
outperforms state-of-the-art baselines for generating less egocentric, more
human-like, and higher quality responses in both automatic and human
evaluations.Comment: Accepted by SIGIR 2022, 11 pages, 9 figure