9 research outputs found
DCR-Net: A Deep Co-Interactive Relation Network for Joint Dialog Act Recognition and Sentiment Classification
In dialog system, dialog act recognition and sentiment classification are two
correlative tasks to capture speakers intentions, where dialog act and
sentiment can indicate the explicit and the implicit intentions separately.
Most of the existing systems either treat them as separate tasks or just
jointly model the two tasks by sharing parameters in an implicit way without
explicitly modeling mutual interaction and relation. To address this problem,
we propose a Deep Co-Interactive Relation Network (DCR-Net) to explicitly
consider the cross-impact and model the interaction between the two tasks by
introducing a co-interactive relation layer. In addition, the proposed relation
layer can be stacked to gradually capture mutual knowledge with multiple steps
of interaction. Especially, we thoroughly study different relation layers and
their effects. Experimental results on two public datasets (Mastodon and
Dailydialog) show that our model outperforms the state-of-the-art joint model
by 4.3% and 3.4% in terms of F1 score on dialog act recognition task, 5.7% and
12.4% on sentiment classification respectively. Comprehensive analysis
empirically verifies the effectiveness of explicitly modeling the relation
between the two tasks and the multi-steps interaction mechanism. Finally, we
employ the Bidirectional Encoder Representation from Transformer (BERT) in our
framework, which can further boost our performance in both tasks.Comment: Accepted by AAAI2020 (Oral
Co-GAT: A Co-Interactive Graph Attention Network for Joint Dialog Act Recognition and Sentiment Classification
In a dialog system, dialog act recognition and sentiment classification are
two correlative tasks to capture speakers intentions, where dialog act and
sentiment can indicate the explicit and the implicit intentions separately. The
dialog context information (contextual information) and the mutual interaction
information are two key factors that contribute to the two related tasks.
Unfortunately, none of the existing approaches consider the two important
sources of information simultaneously. In this paper, we propose a
Co-Interactive Graph Attention Network (Co-GAT) to jointly perform the two
tasks. The core module is a proposed co-interactive graph interaction layer
where a cross-utterances connection and a cross-tasks connection are
constructed and iteratively updated with each other, achieving to consider the
two types of information simultaneously. Experimental results on two public
datasets show that our model successfully captures the two sources of
information and achieve the state-of-the-art performance.
In addition, we find that the contributions from the contextual and mutual
interaction information do not fully overlap with contextualized word
representations (BERT, Roberta, XLNet).Comment: Accepted by AAAI2021 (Long Paper). arXiv admin note: text overlap
with arXiv:2008.0691
A Unifying Framework of Bilinear LSTMs
This paper presents a novel unifying framework of bilinear LSTMs that can
represent and utilize the nonlinear interaction of the input features present
in sequence datasets for achieving superior performance over a linear LSTM and
yet not incur more parameters to be learned. To realize this, our unifying
framework allows the expressivity of the linear vs. bilinear terms to be
balanced by correspondingly trading off between the hidden state vector size
vs. approximation quality of the weight matrix in the bilinear term so as to
optimize the performance of our bilinear LSTM, while not incurring more
parameters to be learned. We empirically evaluate the performance of our
bilinear LSTM in several language-based sequence learning tasks to demonstrate
its general applicability