156 research outputs found
Lattice CNNs for Matching Based Chinese Question Answering
Short text matching often faces the challenges that there are great word mismatch and expression diversity between the two texts, which would be further aggravated in languages like Chinese where there is no natural space to segment words explicitly. In this paper, we propose a novel lattice based CNN model (LCNs) to utilize multi-granularity information inherent in the word lattice while maintaining strong ability to deal with the introduced noisy information for matching based question answering in Chinese. We conduct extensive experiments on both document based question answering and knowledge based question answering tasks, and experimental results show that the LCNs models can significantly outperform the state-of-the-art matching models and strong baselines by taking advantages of better ability to distill rich but discriminative information from the word lattice input
Gated Convolutional Bidirectional Attention-based Model for Off-topic Spoken Response Detection
Off-topic spoken response detection, the task aiming at predicting whether a
response is off-topic for the corresponding prompt, is important for an
automated speaking assessment system. In many real-world educational
applications, off-topic spoken response detectors are required to achieve high
recall for off-topic responses not only on seen prompts but also on prompts
that are unseen during training. In this paper, we propose a novel approach for
off-topic spoken response detection with high off-topic recall on both seen and
unseen prompts. We introduce a new model, Gated Convolutional Bidirectional
Attention-based Model (GCBiA), which applies bi-attention mechanism and
convolutions to extract topic words of prompts and key-phrases of responses,
and introduces gated unit and residual connections between major layers to
better represent the relevance of responses and prompts. Moreover, a new
negative sampling method is proposed to augment training data. Experiment
results demonstrate that our novel approach can achieve significant
improvements in detecting off-topic responses with extremely high on-topic
recall, for both seen and unseen prompts.Comment: ACL2020 long pape
Graph Neural Networks for Natural Language Processing: A Survey
Deep learning has become the dominant approach in coping with various tasks
in Natural LanguageProcessing (NLP). Although text inputs are typically
represented as a sequence of tokens, there isa rich variety of NLP problems
that can be best expressed with a graph structure. As a result, thereis a surge
of interests in developing new deep learning techniques on graphs for a large
numberof NLP tasks. In this survey, we present a comprehensive overview onGraph
Neural Networks(GNNs) for Natural Language Processing. We propose a new
taxonomy of GNNs for NLP, whichsystematically organizes existing research of
GNNs for NLP along three axes: graph construction,graph representation
learning, and graph based encoder-decoder models. We further introducea large
number of NLP applications that are exploiting the power of GNNs and summarize
thecorresponding benchmark datasets, evaluation metrics, and open-source codes.
Finally, we discussvarious outstanding challenges for making the full use of
GNNs for NLP as well as future researchdirections. To the best of our
knowledge, this is the first comprehensive overview of Graph NeuralNetworks for
Natural Language Processing.Comment: 127 page
A Named Entity Recognition Method Enhanced with Lexicon Information and Text Local Feature
At present, Named Entity Recognition (NER) is one of the fundamental tasks for extracting knowledge from traditional Chinese medicine (TCM) texts. The variability of the length of TCM entities and the characteristics of the language of TCM texts lead to ambiguity of TCM entity boundaries. In addition, better extracting and exploiting local features of text can improve the accuracy of named entity recognition. In this paper, we proposed a TCM NER model with lexicon information and text local feature enhancement of text. In this model, a lexicon is introduced to encode the characters in the text to obtain the context-sensitive global semantic representation of the text. The convolutional neural network (CNN) and gate joined collaborative attention network are used to form a text local feature extraction module to capture the important semantic features of local text. Experiments were conducted on two TCM domain datasets and the F1 values are 91.13% and 90.21% respectively
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