Jun Liu


Inductive Relation Prediction with Logical Reasoning Using Contrastive Representations
Yudai Pan | Jun Liu | Lingling Zhang | Tianzhe Zhao | Qika Lin | Xin Hu | Qianying Wang
Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing

Relation prediction in knowledge graphs (KGs) aims at predicting missing relations in incomplete triples, whereas the dominant embedding paradigm has a restriction on handling unseen entities during testing. In the real-world scenario, the inductive setting is more common because entities in the training process are finite. Previous methods capture an inductive ability by implicit logic in KGs. However, it would be challenging to preciously acquire entity-independent relational semantics of compositional logic rules and to deal with the deficient supervision of logic caused by the scarcity of relational semantics. To this end, we propose a novel graph convolutional network (GCN)-based model LogCo with logical reasoning by contrastive representations. LogCo firstly extracts enclosing subgraphs and relational paths between two entities to supply the entity-independence. Then a contrastive strategy for relational path instances and the subgraph is proposed for the issue of deficient supervision. The contrastive representations are learned for a joint training regime. Finally, prediction results and logic rules for reasoning are attained. Comprehensive experiments on twelve inductive datasets show that LogCo achieves outstanding performance comparing with state-of-the-art inductive relation prediction baselines.

MatchPrompt: Prompt-based Open Relation Extraction with Semantic Consistency Guided Clustering
Jiaxin Wang | Lingling Zhang | Jun Liu | Xi Liang | Yujie Zhong | Yaqiang Wu
Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing

Relation clustering is a general approach for open relation extraction (OpenRE). Current methods have two major problems. One is that their good performance relies on large amounts of labeled and pre-defined relational instances for pre-training, which are costly to acquire in reality. The other is that they only focus on learning a high-dimensional metric space to measure the similarity of novel relations and ignore the specific relational representations of clusters. In this work, we propose a new prompt-based framework named MatchPrompt, which can realize OpenRE with efficient knowledge transfer from only a few pre-defined relational instances as well as mine the specific meanings for cluster interpretability. To our best knowledge, we are the first to introduce a prompt-based framework for unlabeled clustering. Experimental results on different datasets show that MatchPrompt achieves the new SOTA results for OpenRE.


Analyzing the Forgetting Problem in Pretrain-Finetuning of Open-domain Dialogue Response Models
Tianxing He | Jun Liu | Kyunghyun Cho | Myle Ott | Bing Liu | James Glass | Fuchun Peng
Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume

In this work, we study how the finetuning stage in the pretrain-finetune framework changes the behavior of a pretrained neural language generator. We focus on the transformer encoder-decoder model for the open-domain dialogue response generation task. Our major finding is that after standard finetuning, the model forgets some of the important language generation skills acquired during large-scale pretraining. We demonstrate the forgetting phenomenon through a set of detailed behavior analysis from the perspectives of knowledge transfer, context sensitivity, and function space projection. As a preliminary attempt to alleviate the forgetting problem, we propose an intuitive finetuning strategy named “mix-review”. We find that mix-review effectively regularizes the finetuning process, and the forgetting problem is alleviated to some extent. Finally, we discuss interesting behavior of the resulting dialogue model and its implications.


Sentence Suggestion of Japanese Functional Expressions for Chinese-speaking Learners
Jun Liu | Hiroyuki Shindo | Yuji Matsumoto
Proceedings of ACL 2018, System Demonstrations

We present a computer-assisted learning system, Jastudy, which is particularly designed for Chinese-speaking learners of Japanese as a second language (JSL) to learn Japanese functional expressions with suggestion of appropriate example sentences. The system automatically recognizes Japanese functional expressions using a free Japanese morphological analyzer MeCab, which is retrained on a new Conditional Random Fields (CRF) model. In order to select appropriate example sentences, we apply a pairwise-based machine learning tool, Support Vector Machine for Ranking (SVMrank) to estimate the complexity of the example sentences using Japanese–Chinese homographs as an important feature. In addition, we cluster the example sentences that contain Japanese functional expressions with two or more meanings and usages, based on part-of-speech, conjugation forms of verbs and semantic attributes, using the K-means clustering algorithm in Scikit-Learn. Experimental results demonstrate the effectiveness of our approach.

Automatic Error Correction on Japanese Functional Expressions Using Character-based Neural Machine Translation
Jun Liu | Fei Cheng | Yiran Wang | Hiroyuki Shindo | Yuji Matsumoto
Proceedings of the 32nd Pacific Asia Conference on Language, Information and Computation


Sentence Complexity Estimation for Chinese-speaking Learners of Japanese
Jun Liu | Yuji Matsumoto
Proceedings of the 31st Pacific Asia Conference on Language, Information and Computation


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Simplification of Example Sentences for Learners of Japanese Functional Expressions
Jun Liu | Yuji Matsumoto
Proceedings of the 3rd Workshop on Natural Language Processing Techniques for Educational Applications (NLPTEA2016)

Learning functional expressions is one of the difficulties for language learners, since functional expressions tend to have multiple meanings and complicated usages in various situations. In this paper, we report an experiment of simplifying example sentences of Japanese functional expressions especially for Chinese-speaking learners. For this purpose, we developed “Japanese Functional Expressions List” and “Simple Japanese Replacement List”. To evaluate the method, we conduct a small-scale experiment with Chinese-speaking learners on the effectiveness of the simplified example sentences. The experimental results indicate that simplified sentences are helpful in learning Japanese functional expressions.


CMDMC: A Diachronic Digital Museum of Chinese Mandarin
Min Hou | Yu Zou | Yonglin Teng | Wei He | Yan Wang | Jun Liu | Jiyuan Wu
CIPS-SIGHAN Joint Conference on Chinese Language Processing