135 research outputs found
Component-Enhanced Chinese Character Embeddings
Distributed word representations are very useful for capturing semantic
information and have been successfully applied in a variety of NLP tasks,
especially on English. In this work, we innovatively develop two
component-enhanced Chinese character embedding models and their bigram
extensions. Distinguished from English word embeddings, our models explore the
compositions of Chinese characters, which often serve as semantic indictors
inherently. The evaluations on both word similarity and text classification
demonstrate the effectiveness of our models.Comment: 6 pages, 2 figures, conference, EMNLP 201
Implicit Discourse Relation Classification via Multi-Task Neural Networks
Without discourse connectives, classifying implicit discourse relations is a
challenging task and a bottleneck for building a practical discourse parser.
Previous research usually makes use of one kind of discourse framework such as
PDTB or RST to improve the classification performance on discourse relations.
Actually, under different discourse annotation frameworks, there exist multiple
corpora which have internal connections. To exploit the combination of
different discourse corpora, we design related discourse classification tasks
specific to a corpus, and propose a novel Convolutional Neural Network embedded
multi-task learning system to synthesize these tasks by learning both unique
and shared representations for each task. The experimental results on the PDTB
implicit discourse relation classification task demonstrate that our model
achieves significant gains over baseline systems.Comment: This is the pre-print version of a paper accepted by AAAI-1
Query and Output: Generating Words by Querying Distributed Word Representations for Paraphrase Generation
Most recent approaches use the sequence-to-sequence model for paraphrase
generation. The existing sequence-to-sequence model tends to memorize the words
and the patterns in the training dataset instead of learning the meaning of the
words. Therefore, the generated sentences are often grammatically correct but
semantically improper. In this work, we introduce a novel model based on the
encoder-decoder framework, called Word Embedding Attention Network (WEAN). Our
proposed model generates the words by querying distributed word representations
(i.e. neural word embeddings), hoping to capturing the meaning of the according
words. Following previous work, we evaluate our model on two
paraphrase-oriented tasks, namely text simplification and short text
abstractive summarization. Experimental results show that our model outperforms
the sequence-to-sequence baseline by the BLEU score of 6.3 and 5.5 on two
English text simplification datasets, and the ROUGE-2 F1 score of 5.7 on a
Chinese summarization dataset. Moreover, our model achieves state-of-the-art
performances on these three benchmark datasets.Comment: arXiv admin note: text overlap with arXiv:1710.0231
Interactive Attention Networks for Aspect-Level Sentiment Classification
Aspect-level sentiment classification aims at identifying the sentiment
polarity of specific target in its context. Previous approaches have realized
the importance of targets in sentiment classification and developed various
methods with the goal of precisely modeling their contexts via generating
target-specific representations. However, these studies always ignore the
separate modeling of targets. In this paper, we argue that both targets and
contexts deserve special treatment and need to be learned their own
representations via interactive learning. Then, we propose the interactive
attention networks (IAN) to interactively learn attentions in the contexts and
targets, and generate the representations for targets and contexts separately.
With this design, the IAN model can well represent a target and its collocative
context, which is helpful to sentiment classification. Experimental results on
SemEval 2014 Datasets demonstrate the effectiveness of our model.Comment: Accepted by IJCAI 201
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