155 research outputs found
Commonsense knowledge enhanced memory network for stance classification
Stance classification aims at identifying, in the text, the attitude toward the given targets as favorable, negative, or unrelated. In existing models for stance classification, only textual representation is leveraged, while commonsense knowledge is ignored. In order to better incorporate commonsense knowledge into stance classification, we propose a novel model named commonsense knowledge enhanced memory network, which jointly represents textual and commonsense knowledge representation of given target and text. The textual memory module in our model treats the textual representation as memory vectors, and uses attention mechanism to embody the important parts. For commonsense knowledge memory module, we jointly leverage the entity and relation embeddings learned by TransE model to take full advantage of constraints of the knowledge graph. Experimental results on the SemEval dataset show that the combination of the commonsense knowledge memory and textual memory can improve stance classification
Response Ranking with Deep Matching Networks and External Knowledge in Information-seeking Conversation Systems
Intelligent personal assistant systems with either text-based or voice-based
conversational interfaces are becoming increasingly popular around the world.
Retrieval-based conversation models have the advantages of returning fluent and
informative responses. Most existing studies in this area are on open domain
"chit-chat" conversations or task / transaction oriented conversations. More
research is needed for information-seeking conversations. There is also a lack
of modeling external knowledge beyond the dialog utterances among current
conversational models. In this paper, we propose a learning framework on the
top of deep neural matching networks that leverages external knowledge for
response ranking in information-seeking conversation systems. We incorporate
external knowledge into deep neural models with pseudo-relevance feedback and
QA correspondence knowledge distillation. Extensive experiments with three
information-seeking conversation data sets including both open benchmarks and
commercial data show that, our methods outperform various baseline methods
including several deep text matching models and the state-of-the-art method on
response selection in multi-turn conversations. We also perform analysis over
different response types, model variations and ranking examples. Our models and
research findings provide new insights on how to utilize external knowledge
with deep neural models for response selection and have implications for the
design of the next generation of information-seeking conversation systems.Comment: Accepted by the 41th International ACM SIGIR Conference on Research
and Development in Information Retrieval (SIGIR 2018), Ann Arbor, Michigan,
U.S.A. July 8-12, 2018 (Full Oral Paper
Ranking and Selecting Multi-Hop Knowledge Paths to Better Predict Human Needs
To make machines better understand sentiments, research needs to move from
polarity identification to understanding the reasons that underlie the
expression of sentiment. Categorizing the goals or needs of humans is one way
to explain the expression of sentiment in text. Humans are good at
understanding situations described in natural language and can easily connect
them to the character's psychological needs using commonsense knowledge. We
present a novel method to extract, rank, filter and select multi-hop relation
paths from a commonsense knowledge resource to interpret the expression of
sentiment in terms of their underlying human needs. We efficiently integrate
the acquired knowledge paths in a neural model that interfaces context
representations with knowledge using a gated attention mechanism. We assess the
model's performance on a recently published dataset for categorizing human
needs. Selectively integrating knowledge paths boosts performance and
establishes a new state-of-the-art. Our model offers interpretability through
the learned attention map over commonsense knowledge paths. Human evaluation
highlights the relevance of the encoded knowledge
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