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Zhao Yan

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9

AAAI Conference 2021 Conference Paper

A Unified Multi-Task Learning Framework for Joint Extraction of Entities and Relations

  • Tianyang Zhao
  • Zhao Yan
  • Yunbo Cao
  • Zhoujun Li

Joint extraction of entities and relations has achieved great success in recent year by task decomposition and multi-task learning. Previous works effectively perform the task through different extraction order, such as relation-last, relation-first and relation-middle manner. However, these methods still suffer from the template-dependency, non-entity detection and non-predefined relation prediction problem. To overcome these challenges, in this paper, we propose a unified multitask learning framework, which decomposes the task into three interacted sub-tasks. Specifically, we first introduce the type-attentional method for subject extraction to provide prior type information explicitly. Then, the subject-aware relation prediction is presented to select useful relations based on the combination of global and local semantics. Third, we propose a question generation based QA method for object extraction to obtain diverse queries automatically. Notably, our method detects subjects or objects without relying on NER models and thus it is capable of dealing with the non-entity scenario. Finally, three sub-tasks are integrated into a unified model through parameter sharing. Extensive experiments demonstrate that the proposed framework outperforms all the baseline methods on four benchmark datasets, and further achieves excellent performance for non-predefined relations.

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IJCAI Conference 2021 Conference Paper

Correlation-Guided Representation for Multi-Label Text Classification

  • Qian-Wen Zhang
  • Ximing Zhang
  • Zhao Yan
  • Ruifang Liu
  • Yunbo Cao
  • Min-Ling Zhang

Multi-label text classification is an essential task in natural language processing. Existing multi-label classification models generally consider labels as categorical variables and ignore the exploitation of label semantics. In this paper, we view the task as a correlation-guided text representation problem: an attention-based two-step framework is proposed to integrate text information and label semantics by jointly learning words and labels in the same space. In this way, we aim to capture high-order label-label correlations as well as context-label correlations. Specifically, the proposed approach works by learning token-level representations of words and labels globally through a multi-layer Transformer and constructing an attention vector through word-label correlation matrix to generate the text representation. It ensures that relevant words receive higher weights than irrelevant words and thus directly optimizes the classification performance. Extensive experiments over benchmark multi-label datasets clearly validate the effectiveness of the proposed approach, and further analysis demonstrates that it is competitive in both predicting low-frequency labels and convergence speed.

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AAAI Conference 2021 System Paper

MMKE: A Multi-Model Knowledge Extraction System from Unstructured Texts

  • Qian-Wen Zhang
  • Zhao Yan
  • Tianyang Zhao
  • Shi-Wei Zhang
  • Meng Yao
  • Meng-Liang Rao
  • Yunbo Cao

In this work, we present a Multi-Model Knowledge Extraction (MMKE) System which consists of two unstructured text extraction models (RelationSO model and SubjectRO model) based on a multi-task learning framework. Instead of recognizing entity first and then predicting relationships between entity pairs in previous works, MMKE detects subject and corresponding relationships before extracting objects to cope with the diverse object-type problem, overlapping problem and non-predefined relation problem. Our system accepts unstructured text as input, from which it automatically extracts knowledge in the form of (subject, relation, object) triples. More importantly, we incorporate a number of userfriendly extraction functionalities, such as multi-format uploading, one-click extractions, knowledge editing and graphical displays. The demonstration video is available at this link: https: //youtu. be/HtOPJrGhSxk.

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IJCAI Conference 2020 Conference Paper

Asking Effective and Diverse Questions: A Machine Reading Comprehension based Framework for Joint Entity-Relation Extraction

  • Tianyang Zhao
  • Zhao Yan
  • Yunbo Cao
  • Zhoujun Li

Recent advances cast the entity-relation extraction to a multi-turn question answering (QA) task and provide an effective solution based on the machine reading comprehension (MRC) models. However, they use a single question to characterize the meaning of entities and relations, which is intuitively not enough because of the variety of context semantics. Meanwhile, existing models enumerate all relation types to generate questions, which is inefficient and easily leads to confusing questions. In this paper, we improve the existing MRC-based entity-relation extraction model through diverse question answering. First, a diversity question answering mechanism is introduced to detect entity spans and two answering selection strategies are designed to integrate different answers. Then, we propose to predict a subset of potential relations and filter out irrelevant ones to generate questions effectively. Finally, entity and relation extractions are integrated in an end-to-end way and optimized through joint learning. Experiment results show that the proposed method significantly outperforms baseline models, which improves the relation F1 to 62. 1% (+1. 9%) on ACE05 and 71. 9% (+3. 0%) on CoNLL04. Our implementation is available at https: //github. com/TanyaZhao/MRC4ERE.

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AAAI Conference 2018 Conference Paper

Assertion-Based QA With Question-Aware Open Information Extraction

  • Zhao Yan
  • Duyu Tang
  • Nan Duan
  • Shujie Liu
  • Wendi Wang
  • Daxin Jiang
  • Ming Zhou
  • Zhoujun Li

We present assertion based question answering (ABQA), an open domain question answering task that takes a question and a passage as inputs, and outputs a semi-structured assertion consisting of a subject, a predicate and a list of arguments. An assertion conveys more evidences than a short answer span in reading comprehension, and it is more concise than a tedious passage in passage-based QA. These advantages make ABQA more suitable for human-computer interaction scenarios such as voice-controlled speakers. Further progress towards improving ABQA requires richer supervised dataset and powerful models of text understanding. To remedy this, we introduce a new dataset called WebAssertions, which includes hand-annotated QA labels for 358, 427 assertions in 55, 960 web passages. To address ABQA, we develop both generative and extractive approaches. The backbone of our generative approach is sequence to sequence learning. In order to capture the structure of the output assertion, we introduce a hierarchical decoder that first generates the structure of the assertion and then generates the words of each field. The extractive approach is based on learning to rank. Features at different levels of granularity are designed to measure the semantic relevance between a question and an assertion. Experimental results show that our approaches have the ability to infer question-aware assertions from a passage. We further evaluate our approaches by incorporating the ABQA results as additional features in passage-based QA. Results on two datasets show that ABQA features significantly improve the accuracy on passage-based QA.

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AAAI Conference 2018 Conference Paper

Table-to-Text: Describing Table Region With Natural Language

  • Junwei Bao
  • Duyu Tang
  • Nan Duan
  • Zhao Yan
  • Yuanhua Lv
  • Ming Zhou
  • Tiejun Zhao

In this paper, we present a generative model to generate a natural language sentence describing a table region, e. g. , a row. The model maps a row from a table to a continuous vector and then generates a natural language sentence by leveraging the semantics of a table. To deal with rare words appearing in a table, we develop a flexible copying mechanism that selectively replicates contents from the table in the output sequence. Extensive experiments demonstrate the accuracy of the model and the power of the copying mechanism. On two synthetic datasets, WIKIBIO and SIMPLEQUESTIONS, our model improves the current state-of-the-art BLEU-4 score from 34. 70 to 40. 26 and from 33. 32 to 39. 12, respectively. Furthermore, we introduce an open-domain dataset WIK- ITABLETEXT including 13, 318 explanatory sentences for 4, 962 tables. Our model achieves a BLEU-4 score of 38. 23, which outperforms template based and language model based approaches.

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AAAI Conference 2017 Conference Paper

Building Task-Oriented Dialogue Systems for Online Shopping

  • Zhao Yan
  • Nan Duan
  • Peng Chen
  • Ming Zhou
  • Jianshe Zhou
  • Zhoujun Li

We present a general solution towards building task-oriented dialogue systems for online shopping, aiming to assist online customers in completing various purchase-related tasks, such as searching products and answering questions, in a natural language conversation manner. As a pioneering work, we show what & how existing natural language processing techniques, data resources, and crowdsourcing can be leveraged to build such task-oriented dialogue systems for E-commerce usage. To demonstrate its effectiveness, we integrate our system into a mobile online shopping application. To the best of our knowledge, this is the first time that an dialogue system in Chinese is practically used in online shopping scenario with millions of real consumers. Interesting and insightful observations are shown in the experimental part, based on the analysis of human-bot conversation log. Several current challenges are also pointed out as our future directions.

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AAAI Conference 2016 Conference Paper

Aggregating Inter-Sentence Information to Enhance Relation Extraction

  • Hao Zheng
  • Zhoujun Li
  • Senzhang Wang
  • Zhao Yan
  • Jianshe Zhou

Previous work for relation extraction from free text is mainly based on intra-sentence information. As relations might be mentioned across sentences, inter-sentence information can be leveraged to improve distantly supervised relation extraction. To effectively exploit inter-sentence information, we propose a ranking-based approach, which first learns a scoring function based on a listwise learning-to-rank model and then uses it for multi-label relation extraction. Experimental results verify the effectiveness of our method for aggregating information across sentences. Additionally, to further improve the ranking of high-quality extractions, we propose an effective method to rank relations from different entity pairs. This method can be easily integrated into our overall relation extraction framework, and boosts the precision significantly.

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AAAI Conference 2015 Conference Paper

Burst Time Prediction in Cascades

  • Senzhang Wang
  • Zhao Yan
  • Xia Hu
  • Philip S. Yu
  • Zhoujun Li

Studying the bursty nature of cascades in social media is practically important in many applications such as product sales prediction, disaster relief, and stock market prediction. Although the cascade volume prediction has been extensively studied, how to predict when a burst will come remains an open problem. It is challenging to predict the time of the burst due to the “quick rise and fall” pattern and the diverse time spans of the cascades. To this end, this paper proposes a classification based approach for burst time prediction by utilizing and modeling rich knowledge in information diffusion. Particularly, we first propose a time window based approach to predict in which time window the burst will appear. This paves the way to transform the time prediction task to a classification problem. To address the challenge that the original time series data of the cascade popularity only are not sufficient for predicting cascades with diverse magnitudes and time spans, we explore rich information diffusion related knowledge and model them in a scale-independent manner. Extensive experiments on a Sina Weibo reposting dataset demonstrate the superior performance of the proposed approach in accurately predicting the burst time of posts.

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