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Yijun Xiao

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3 papers

1 author row

AAAI Conference 2019 Conference Paper

Quantifying Uncertainties in Natural Language Processing Tasks

Yijun Xiao
William Yang Wang

Reliable uncertainty quantification is a first step towards building explainable, transparent, and accountable artificial intelligent systems. Recent progress in Bayesian deep learning has made such quantification realizable. In this paper, we propose novel methods to study the benefits of characterizing model and data uncertainties for natural language processing (NLP) tasks. With empirical experiments on sentiment analysis, named entity recognition, and language modeling using convolutional and recurrent neural network models, we show that explicitly modeling uncertainties is not only necessary to measure output confidence levels, but also useful at enhancing model performances in various NLP tasks.

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AAAI Conference 2018 Conference Paper

Elastic Responding Machine for Dialog Generation with Dynamically Mechanism Selecting

Ganbin Zhou
Ping Luo
Yijun Xiao
Fen Lin
Bo Chen
Qing He

Neural models aiming at generating meaningful and diverse response is attracting increasing attention over recent years. For a given post, the conventional encoder-decoder models tend to learn high-frequency but trivial responses, or are dif- ﬁcult to determine which speaking styles are suitable to generate responses. To address this issue, we propose the elastic responding machine (ERM), which is based on a proposed encoder-diverter-ﬁlter-decoder framework. ERM models the multiple responding mechanisms to not only generate acceptable responses for a given post but also improve the diversity of responses. Here, the mechanisms could be regraded as some latent variables, and for a given post different responses may be generated by different mechanisms. The experiments demonstrate the quality and diversity of the generated responses, intuitively show how the learned model controls response mechanism when responding, and reveal some underlying relationship between mechanism and language style.

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AAAI Conference 2018 Conference Paper

Tree-Structured Neural Machine for Linguistics-Aware Sentence Generation

Ganbin Zhou
Ping Luo
Rongyu Cao
Yijun Xiao
Fen Lin
Bo Chen
Qing He

Different from other sequential data, sentences in natural language are structured by linguistic grammars. Previous generative conversational models with chain-structured decoder ignore this structure in human language and might generate plausible responses with less satisfactory relevance and ﬂuency. In this study, we aim to incorporate the results from linguistic analysis into the process of sentence generation for high-quality conversation generation. Speciﬁcally, we use a dependency parser to transform each response sentence into a dependency tree and construct a training corpus of sentencetree pairs. A tree-structured decoder is developed to learn the mapping from a sentence to its tree, where different types of hidden states are used to depict the local dependencies from an internal tree node to its children. For training acceleration, we propose a tree canonicalization method, which transforms trees into equivalent ternary trees. Then, with a proposed tree-structured search method, the model is able to generate the most probable responses in the form of dependency trees, which are ﬁnally ﬂattened into sequences as the system output. Experimental results demonstrate that the proposed X2TREE framework outperforms baseline methods over 11. 15% increase of acceptance ratio.

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