2 research outputs found
Attention Is All You Need for Chinese Word Segmentation
Taking greedy decoding algorithm as it should be, this work focuses on
further strengthening the model itself for Chinese word segmentation (CWS),
which results in an even more fast and more accurate CWS model. Our model
consists of an attention only stacked encoder and a light enough decoder for
the greedy segmentation plus two highway connections for smoother training, in
which the encoder is composed of a newly proposed Transformer variant,
Gaussian-masked Directional (GD) Transformer, and a biaffine attention scorer.
With the effective encoder design, our model only needs to take unigram
features for scoring. Our model is evaluated on SIGHAN Bakeoff benchmark
datasets. The experimental results show that with the highest segmentation
speed, the proposed model achieves new state-of-the-art or comparable
performance against strong baselines in terms of strict closed test setting.Comment: 11 pages, to appear in EMNLP 2020 as a long pape
Dialogue Act Classification in Group Chats with DAG-LSTMs
Dialogue act (DA) classification has been studied for the past two decades
and has several key applications such as workflow automation and conversation
analytics. Researchers have used, to address this problem, various traditional
machine learning models, and more recently deep neural network models such as
hierarchical convolutional neural networks (CNNs) and long short-term memory
(LSTM) networks. In this paper, we introduce a new model architecture,
directed-acyclic-graph LSTM (DAG-LSTM) for DA classification. A DAG-LSTM
exploits the turn-taking structure naturally present in a multi-party
conversation, and encodes this relation in its model structure. Using the STAC
corpus, we show that the proposed method performs roughly 0.8% better in
accuracy and 1.2% better in macro-F1 score when compared to existing methods.
The proposed method is generic and not limited to conversation applications.Comment: Appeared in SIGIR 2019 Workshop on Conversational Interaction System