11,467 research outputs found
Learning to Attend, Copy, and Generate for Session-Based Query Suggestion
Users try to articulate their complex information needs during search
sessions by reformulating their queries. To make this process more effective,
search engines provide related queries to help users in specifying the
information need in their search process. In this paper, we propose a
customized sequence-to-sequence model for session-based query suggestion. In
our model, we employ a query-aware attention mechanism to capture the structure
of the session context. is enables us to control the scope of the session from
which we infer the suggested next query, which helps not only handle the noisy
data but also automatically detect session boundaries. Furthermore, we observe
that, based on the user query reformulation behavior, within a single session a
large portion of query terms is retained from the previously submitted queries
and consists of mostly infrequent or unseen terms that are usually not included
in the vocabulary. We therefore empower the decoder of our model to access the
source words from the session context during decoding by incorporating a copy
mechanism. Moreover, we propose evaluation metrics to assess the quality of the
generative models for query suggestion. We conduct an extensive set of
experiments and analysis. e results suggest that our model outperforms the
baselines both in terms of the generating queries and scoring candidate queries
for the task of query suggestion.Comment: Accepted to be published at The 26th ACM International Conference on
Information and Knowledge Management (CIKM2017
Dialogue Act Recognition via CRF-Attentive Structured Network
Dialogue Act Recognition (DAR) is a challenging problem in dialogue
interpretation, which aims to attach semantic labels to utterances and
characterize the speaker's intention. Currently, many existing approaches
formulate the DAR problem ranging from multi-classification to structured
prediction, which suffer from handcrafted feature extensions and attentive
contextual structural dependencies. In this paper, we consider the problem of
DAR from the viewpoint of extending richer Conditional Random Field (CRF)
structural dependencies without abandoning end-to-end training. We incorporate
hierarchical semantic inference with memory mechanism on the utterance
modeling. We then extend structured attention network to the linear-chain
conditional random field layer which takes into account both contextual
utterances and corresponding dialogue acts. The extensive experiments on two
major benchmark datasets Switchboard Dialogue Act (SWDA) and Meeting Recorder
Dialogue Act (MRDA) datasets show that our method achieves better performance
than other state-of-the-art solutions to the problem. It is a remarkable fact
that our method is nearly close to the human annotator's performance on SWDA
within 2% gap.Comment: 10 pages, 4figure
Hierarchical Interaction Networks with Rethinking Mechanism for Document-level Sentiment Analysis
Document-level Sentiment Analysis (DSA) is more challenging due to vague
semantic links and complicate sentiment information. Recent works have been
devoted to leveraging text summarization and have achieved promising results.
However, these summarization-based methods did not take full advantage of the
summary including ignoring the inherent interactions between the summary and
document. As a result, they limited the representation to express major points
in the document, which is highly indicative of the key sentiment. In this
paper, we study how to effectively generate a discriminative representation
with explicit subject patterns and sentiment contexts for DSA. A Hierarchical
Interaction Networks (HIN) is proposed to explore bidirectional interactions
between the summary and document at multiple granularities and learn
subject-oriented document representations for sentiment classification.
Furthermore, we design a Sentiment-based Rethinking mechanism (SR) by refining
the HIN with sentiment label information to learn a more sentiment-aware
document representation. We extensively evaluate our proposed models on three
public datasets. The experimental results consistently demonstrate the
effectiveness of our proposed models and show that HIN-SR outperforms various
state-of-the-art methods.Comment: 17 pages, accepted by ECML-PKDD 202
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