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Muhua Zhu

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6 papers

1 author row

IJCAI Conference 2021 Conference Paper

Improving Text Generation with Dynamic Masking and Recovering

Zhidong Liu
Junhui Li
Muhua Zhu

Due to different types of inputs, diverse text generation tasks may adopt different encoder-decoder frameworks. Thus most existing approaches that aim to improve the robustness of certain generation tasks are input-relevant, and may not work well for other generation tasks. Alternatively, in this paper we present a universal approach to enhance the language representation for text generation on the base of generic encoder-decoder frameworks. This is done from two levels. First, we introduce randomness by randomly masking some percentage of tokens on the decoder side when training the models. In this way, instead of using ground truth history context, we use its corrupted version to predict the next token. Then we propose an auxiliary task to properly recover those masked tokens. Experimental results on several text generation tasks including machine translation (MT), AMR-to-text generation, and image captioning show that the proposed approach can significantly improve over competitive baselines without using any task-specific techniques. This suggests the effectiveness and generality of our proposed approach.

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IJCAI Conference 2020 Conference Paper

Learning with Noise: Improving Distantly-Supervised Fine-grained Entity Typing via Automatic Relabeling

Haoyu Zhang
Dingkun Long
Guangwei Xu
Muhua Zhu
Pengjun Xie
Fei Huang
Ji Wang

Fine-grained entity typing (FET) is a fundamental task for various entity-leveraging applications. Although great success has been made, existing systems still have challenges in handling noisy samples in training data introduced by distant supervision methods. To address these noise, previous studies either focus on processing the clean samples (i, e. , have only one label) and noisy samples (i, e. , have multiple labels) with different strategies or filtering the noisy labels based on the assumption that the distantly-supervised label set certainly contains the correct type label. In this paper, we propose a probabilistic automatic relabeling method which treats all training samples uniformly. Our method aims to estimate the pseudo-truth label distribution of each sample, and the pseudo-truth distribution will be treated as part of trainable parameters which are jointly updated during the training process. The proposed approach does not rely on any prerequisite or extra supervision, making it effective on real applications. Experiments on several benchmarks show that our method outperforms previous approaches and alleviates the noisy labeling problem.

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AAAI Conference 2019 Conference Paper

Deep Cascade Multi-Task Learning for Slot Filling in Online Shopping Assistant

Yu Gong
Xusheng Luo
Yu Zhu
Wenwu Ou
Zhao Li
Muhua Zhu
Kenny Q. Zhu
Lu Duan

Slot filling is a critical task in natural language understanding (NLU) for dialog systems. State-of-the-art approaches treat it as a sequence labeling problem and adopt such models as BiLSTM-CRF. While these models work relatively well on standard benchmark datasets, they face challenges in the context of E-commerce where the slot labels are more informative and carry richer expressions. In this work, inspired by the unique structure of E-commerce knowledge base, we propose a novel multi-task model with cascade and residual connections, which jointly learns segment tagging, named entity tagging and slot filling. Experiments show the effectiveness of the proposed cascade and residual structures. Our model has a 14. 6% advantage in F1 score over the strong baseline methods on a new Chinese E-commerce shopping assistant dataset, while achieving competitive accuracies on a standard dataset. Furthermore, online test deployed on such dominant E-commerce platform shows 130% improvement on accuracy of understanding user utterances. Our model has already gone into production in the E-commerce platform.

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IJCAI Conference 2019 Conference Paper

Modeling Source Syntax and Semantics for Neural AMR Parsing

DongLai Ge
Junhui Li
Muhua Zhu
Shoushan Li

Sequence-to-sequence (seq2seq) approaches formalize Abstract Meaning Representation (AMR) parsing as a translation task from a source sentence to a target AMR graph. However, previous studies generally model a source sentence as a word sequence but ignore the inherent syntactic and semantic information in the sentence. In this paper, we propose two effective approaches to explicitly modeling source syntax and semantics into neural seq2seq AMR parsing. The first approach linearizes source syntactic and semantic structure into a mixed sequence of words, syntactic labels, and semantic labels, while in the second approach we propose a syntactic and semantic structure-aware encoding scheme through a self-attentive model to explicitly capture syntactic and semantic relations between words. Experimental results on an English benchmark dataset show that our two approaches achieve significant improvement of 3. 1% and 3. 4% F1 scores over a strong seq2seq baseline.

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AAAI Conference 2019 Conference Paper

Non-Compensatory Psychological Models for Recommender Systems

Chen Lin
Xiaolin Shen
Si Chen
Muhua Zhu
Yanghua Xiao

The study of consumer psychology reveals two categories of consumption decision procedures: compensatory rules and non-compensatory rules. Existing recommendation models which are based on latent factor models assume the consumers follow the compensatory rules, i. e. they evaluate an item over multiple aspects and compute a weighted or/and summated score which is used to derive the rating or ranking of the item. However, it has been shown in the literature of consumer behavior that, consumers adopt non-compensatory rules more often than compensatory rules. Our main contribution in this paper is to study the unexplored area of utilizing non-compensatory rules in recommendation models. Our general assumptions are (1) there are K universal hidden aspects. In each evaluation session, only one aspect is chosen as the prominent aspect according to user preference. (2) Evaluations over prominent and non-prominent aspects are non-compensatory. Evaluation is mainly based on item performance on the prominent aspect. For non-prominent aspects the user sets a minimal acceptable threshold. We give a conceptual model for these general assumptions. We show how this conceptual model can be realized in both pointwise rating prediction models and pair-wise ranking prediction models. Experiments on real-world data sets validate that adopting non-compensatory rules improves recommendation performance for both rating and ranking models.

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AAAI Conference 2018 Conference Paper

Improving Sequence-to-Sequence Constituency Parsing

Lemao Liu
Muhua Zhu
Shuming Shi

Sequence-to-sequence constituency parsing casts the treestructured prediction problem as a general sequential problem by top-down tree linearization, and thus it is very easy to train in parallel with distributed facilities. Despite its success, it relies on a probabilistic attention mechanism for a general purpose, which can not guarantee the selected context to be informative in the speciﬁc parsing scenario. Previous work introduced a deterministic attention to select the informative context for sequence-to-sequence parsing, but it is based on the bottom-up linearization even if it was observed that top-down linearization is better than bottom-up linearization for standard sequence-to-sequence constituency parsing. In this paper, we thereby extend the deterministic attention to directly conduct on the top-down tree linearization. Intensive experiments show that our parser delivers substantial improvements over the bottom-up linearization in accuracy, and it achieves 92. 3 Fscore on the Penn English Treebank section 23 and 85. 4 Fscore on the Penn Chinese Treebank test dataset, without reranking or semi-supervised training.

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