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Yafeng Ren

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7 papers

2 author rows

IJCAI Conference 2022 Conference Paper

Conversational Semantic Role Labeling with Predicate-Oriented Latent Graph

Hao Fei
Shengqiong Wu
Meishan Zhang
Yafeng Ren
Donghong Ji

Conversational semantic role labeling (CSRL) is a newly proposed task that uncovers the shallow semantic structures in a dialogue text. Unfortunately several important characteristics of the CSRL task have been overlooked by the existing works, such as the structural information integration, near-neighbor influence. In this work, we investigate the integration of a latent graph for CSRL. We propose to automatically induce a predicate-oriented latent graph (POLar) with a predicate-centered gaussian mechanism, by which the nearer and informative words to the predicate will be allocated with more attention. The POLar structure is then dynamically pruned and refined so as to best fit the task need. We additionally introduce an effective dialogue-level pre-trained language model, CoDiaBERT, for better supporting multiple utterance sentences and handling the speaker coreference issue in CSRL. Our system outperforms best-performing baselines on three benchmark CSRL datasets with big margins, especially achieving over 4% F1 score improvements on the cross-utterance argument detection. Further analyses are presented to better understand the effectiveness of our proposed methods.

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ICML Conference 2022 Conference Paper

Matching Structure for Dual Learning

Hao Fei 0001
Shengqiong Wu
Yafeng Ren
Meishan Zhang

Many natural language processing (NLP) tasks appear in dual forms, which are generally solved by dual learning technique that models the dualities between the coupled tasks. In this work, we propose to further enhance dual learning with structure matching that explicitly builds structural connections in between. Starting with the dual text$\leftrightarrow$text generation, we perform dually-syntactic structure co-echoing of the region of interest (RoI) between the task pair, together with a syntax cross-reconstruction at the decoding side. We next extend the idea to a text$\leftrightarrow$non-text setup, making alignment between the syntactic-semantic structure. Over 2*14 tasks covering 5 dual learning scenarios, the proposed structure matching method shows its significant effectiveness in enhancing existing dual learning. Our method can retrieve the key RoIs that are highly crucial to the task performance. Besides NLP tasks, it is also revealed that our approach has great potential in facilitating more non-text$\leftrightarrow$non-text scenarios.

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IJCAI Conference 2021 Conference Paper

Learn from Syntax: Improving Pair-wise Aspect and Opinion Terms Extraction with Rich Syntactic Knowledge

Shengqiong Wu
Hao Fei
Yafeng Ren
Donghong Ji
Jingye Li

In this paper, we propose to enhance the pair-wise aspect and opinion terms extraction (PAOTE) task by incorporating rich syntactic knowledge. We first build a syntax fusion encoder for encoding syntactic features, including a label-aware graph convolutional network (LAGCN) for modeling the dependency edges and labels, as well as the POS tags unifiedly, and a local-attention module encoding POS tags for better term boundary detection. During pairing, we then adopt Biaffine and Triaffine scoring for high-order aspect-opinion term pairing, in the meantime re-harnessing the syntax-enriched representations in LAGCN for syntactic-aware scoring. Experimental results on four benchmark datasets demonstrate that our model outperforms current state-of-the-art baselines, meanwhile yielding explainable predictions with syntactic knowledge.

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AAAI Conference 2021 Conference Paper

Rethinking Boundaries: End-To-End Recognition of Discontinuous Mentions with Pointer Networks

Hao Fei
Donghong Ji
Bobo Li
Yijiang Liu
Yafeng Ren
Fei Li

A majority of research interests in irregular (e. g. , nested or discontinuous) named entity recognition (NER) have been paid on nested entities, while discontinuous entities received limited attention. Existing work for discontinuous NER, however, either suffers from decoding ambiguity or predicting using token-level local features. In this work, we present an innovative model for discontinuous NER based on pointer networks, where the pointer simultaneously decides whether a token at each decoding frame constitutes an entity mention and where the next constituent token is. Our model has three major merits compared with previous work: (1) The pointer mechanism is memory-augmented, which enhances the mention boundary detection and interactions between the current decision and prior recognized mentions. (2) The encoderdecoder architecture can linearize the complexity of structure prediction, and thus reduce search costs. (3) The model makes every decision using global information, i. e. , by consulting all the input, encoder and previous decoder output in a global view. Experimental results on the CADEC and ShARe13 datasets show that our model outperforms flat and hypergraph models as well as a state-of-the-art transitionbased model for discontinuous NER. Further in-depth analysis demonstrates that our model performs well in recognizing various entities including flat, overlapping and discontinuous ones. More crucially, our model is effective on boundary detection, which is the kernel source to NER.

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AAAI Conference 2020 Conference Paper

Latent Emotion Memory for Multi-Label Emotion Classification

Hao Fei
Yue Zhang
Yafeng Ren
Donghong Ji

Identifying multiple emotions in a sentence is an important research topic. Existing methods usually model the problem as multi-label classiﬁcation task. However, previous methods have two issues, limiting the performance of the task. First, these models do not consider prior emotion distribution in a sentence. Second, they fail to effectively capture the context information closely related to the corresponding emotion. In this paper, we propose a Latent Emotion Memory network (LEM) for multi-label emotion classiﬁcation. The proposed model can learn the latent emotion distribution without external knowledge, and can effectively leverage it into the classiﬁcation network. Experimental results on two benchmark datasets show that the proposed model outperforms strong baselines, achieving the state-of-the-art performance.

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AAAI Conference 2016 Conference Paper

Context-Sensitive Twitter Sentiment Classification Using Neural Network

Yafeng Ren
Yue Zhang
Meishan Zhang
Donghong Ji

Sentiment classiﬁcation on Twitter has attracted increasing research in recent years. Most existing work focuses on feature engineering according to the tweet content itself. In this paper, we propose a contextbased neural network model for Twitter sentiment analysis, incorporating contextualized features from relevant Tweets into the model in the form of word embedding vectors. Experiments on both balanced and unbalanced datasets show that our proposed models outperform the current state-of-the-art.

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AAAI Conference 2016 Conference Paper

Improving Twitter Sentiment Classification Using Topic-Enriched Multi-Prototype Word Embeddings

Yafeng Ren
Yue Zhang
Meishan Zhang
Donghong Ji

It has been shown that learning distributed word representations is highly useful for Twitter sentiment classiﬁcation. Most existing models rely on a single distributed representation for each word. This is problematic for sentiment classi- ﬁcation because words are often polysemous and each word can contain different sentiment polarities under different topics. We address this issue by learning topic-enriched multiprototype word embeddings (TMWE). In particular, we develop two neural networks which 1) learn word embeddings that better capture tweet context by incorporating topic information, and 2) learn topic-enriched multiple prototype embeddings for each word. Experiments on Twitter sentiment benchmark datasets in SemEval 2013 show that TMWE outperforms the top system with hand-crafted features, and the current best neural network model.

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