244 citations · 480 across the 41 of their papers we have counts for
15 papers · 1 filter
CASE: Context-Aware Semantic Expansion
Jialong Han, Aixin Sun, Haisong Zhang +2
In this paper, we define and study a new task called Context-Aware Semantic Expansion (CASE). Given a seed term in a sentential context, we aim to suggest other terms that well fit…
Go From the General to the Particular: Multi-Domain Translation with Domain Transformation Networks
Yong Wang, Longyue Wang, Shuming Shi +2
The key challenge of multi-domain translation lies in simultaneously encoding both the general knowledge shared across domains and the particular knowledge distinctive to each doma…
Neuron Interaction Based Representation Composition for Neural Machine Translation
Jian Li, Xing Wang, Baosong Yang +3
Recent NLP studies reveal that substantial linguistic information can be attributed to single neurons, i.e., individual dimensions of the representation vectors. We hypothesize tha…
A Discrete CVAE for Response Generation on Short-Text Conversation
Jun Gao, Wei Bi, Xiaojiang Liu +3
Neural conversation models such as encoder-decoder models are easy to generate bland and generic responses. Some researchers propose to use the conditional variational autoencoder(…
Semi-supervised Text Style Transfer: Cross Projection in Latent Space
Mingyue Shang, Piji Li, Zhenxin Fu +4
Text style transfer task requires the model to transfer a sentence of one style to another style while retaining its original content meaning, which is a challenging problem that h…
Towards Understanding Neural Machine Translation with Word Importance
Shilin He, Zhaopeng Tu, Xing Wang +3
Although neural machine translation (NMT) has advanced the state-of-the-art on various language pairs, the interpretability of NMT remains unsatisfactory. In this work, we propose…
Multi-Granularity Self-Attention for Neural Machine Translation
Jie Hao, Xing Wang, Shuming Shi +2
Current state-of-the-art neural machine translation (NMT) uses a deep multi-head self-attention network with no explicit phrase information. However, prior work on statistical mach…
Towards Better Modeling Hierarchical Structure for Self-Attention with Ordered Neurons
Jie Hao, Xing Wang, Shuming Shi +2
Recent studies have shown that a hybrid of self-attention networks (SANs) and recurrent neural networks (RNNs) outperforms both individual architectures, while not much is known ab…
Self-Attention with Structural Position Representations
Xing Wang, Zhaopeng Tu, Longyue Wang +1
Although self-attention networks (SANs) have advanced the state-of-the-art on various NLP tasks, one criticism of SANs is their ability of encoding positions of input words (Shaw e…
One Model to Learn Both: Zero Pronoun Prediction and Translation
Longyue Wang, Zhaopeng Tu, Xing Wang +1
Zero pronouns (ZPs) are frequently omitted in pro-drop languages, but should be recalled in non-pro-drop languages. This discourse phenomenon poses a significant challenge for mach…
Fine-Grained Sentence Functions for Short-Text Conversation
Wei Bi, Jun Gao, Xiaojiang Liu +1
Sentence function is an important linguistic feature referring to a user's purpose in uttering a specific sentence. The use of sentence function has shown promising results to impr…
Topic-Aware Neural Keyphrase Generation for Social Media Language
Yue Wang, Jing Li, Hou Pong Chan +3
A huge volume of user-generated content is daily produced on social media. To facilitate automatic language understanding, we study keyphrase prediction, distilling salient informa…
Exploiting Sentential Context for Neural Machine Translation
Xing Wang, Zhaopeng Tu, Longyue Wang +1
In this work, we present novel approaches to exploit sentential context for neural machine translation (NMT). Specifically, we first show that a shallow sentential context extracte…
Microblog Hashtag Generation via Encoding Conversation Contexts
Yue Wang, Jing Li, Irwin King +2
Automatic hashtag annotation plays an important role in content understanding for microblog posts. To date, progress made in this field has been restricted to phrase selection from…
Dynamic Layer Aggregation for Neural Machine Translation with Routing-by-Agreement
Zi-Yi Dou, Zhaopeng Tu, Xing Wang +3
With the promising progress of deep neural networks, layer aggregation has been used to fuse information across layers in various fields, such as computer vision and machine transl…