Jun Araki


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Explicitly Capturing Relations between Entity Mentions via Graph Neural Networks for Domain-specific Named Entity Recognition
Pei Chen | Haibo Ding | Jun Araki | Ruihong Huang
Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 2: Short Papers)

Named entity recognition (NER) is well studied for the general domain, and recent systems have achieved human-level performance for identifying common entity types. However, the NER performance is still moderate for specialized domains that tend to feature complicated contexts and jargonistic entity types. To address these challenges, we propose explicitly connecting entity mentions based on both global coreference relations and local dependency relations for building better entity mention representations. In our experiments, we incorporate entity mention relations by Graph Neural Networks and show that our system noticeably improves the NER performance on two datasets from different domains. We further show that the proposed lightweight system can effectively elevate the NER performance to a higher level even when only a tiny amount of labeled data is available, which is desirable for domain-specific NER.

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How Can We Know When Language Models Know? On the Calibration of Language Models for Question Answering
Zhengbao Jiang | Jun Araki | Haibo Ding | Graham Neubig
Transactions of the Association for Computational Linguistics, Volume 9

Abstract Recent works have shown that language models (LM) capture different types of knowledge regarding facts or common sense. However, because no model is perfect, they still fail to provide appropriate answers in many cases. In this paper, we ask the question, “How can we know when language models know, with confidence, the answer to a particular query?” We examine this question from the point of view of calibration, the property of a probabilistic model’s predicted probabilities actually being well correlated with the probabilities of correctness. We examine three strong generative models—T5, BART, and GPT-2—and study whether their probabilities on QA tasks are well calibrated, finding the answer is a relatively emphatic no. We then examine methods to calibrate such models to make their confidence scores correlate better with the likelihood of correctness through fine-tuning, post-hoc probability modification, or adjustment of the predicted outputs or inputs. Experiments on a diverse range of datasets demonstrate the effectiveness of our methods. We also perform analysis to study the strengths and limitations of these methods, shedding light on further improvements that may be made in methods for calibrating LMs. We have released the code at https://github.com/jzbjyb/lm-calibration.


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X-FACTR: Multilingual Factual Knowledge Retrieval from Pretrained Language Models
Zhengbao Jiang | Antonios Anastasopoulos | Jun Araki | Haibo Ding | Graham Neubig
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

Language models (LMs) have proven surprisingly successful at capturing factual knowledge by completing cloze-style fill-in-the-blank questions such as “Punta Cana is located in _.” However, while knowledge is both written and queried in many languages, studies on LMs’ factual representation ability have almost invariably been performed on English. To assess factual knowledge retrieval in LMs in different languages, we create a multilingual benchmark of cloze-style probes for typologically diverse languages. To properly handle language variations, we expand probing methods from single- to multi-word entities, and develop several decoding algorithms to generate multi-token predictions. Extensive experimental results provide insights about how well (or poorly) current state-of-the-art LMs perform at this task in languages with more or fewer available resources. We further propose a code-switching-based method to improve the ability of multilingual LMs to access knowledge, and verify its effectiveness on several benchmark languages. Benchmark data and code have be released at https://x-factr.github.io.

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Generalizing Natural Language Analysis through Span-relation Representations
Zhengbao Jiang | Wei Xu | Jun Araki | Graham Neubig
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

Natural language processing covers a wide variety of tasks predicting syntax, semantics, and information content, and usually each type of output is generated with specially designed architectures. In this paper, we provide the simple insight that a great variety of tasks can be represented in a single unified format consisting of labeling spans and relations between spans, thus a single task-independent model can be used across different tasks. We perform extensive experiments to test this insight on 10 disparate tasks spanning dependency parsing (syntax), semantic role labeling (semantics), relation extraction (information content), aspect based sentiment analysis (sentiment), and many others, achieving performance comparable to state-of-the-art specialized models. We further demonstrate benefits of multi-task learning, and also show that the proposed method makes it easy to analyze differences and similarities in how the model handles different tasks. Finally, we convert these datasets into a unified format to build a benchmark, which provides a holistic testbed for evaluating future models for generalized natural language analysis.

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How Can We Know What Language Models Know?
Zhengbao Jiang | Frank F. Xu | Jun Araki | Graham Neubig
Transactions of the Association for Computational Linguistics, Volume 8

Recent work has presented intriguing results examining the knowledge contained in language models (LMs) by having the LM fill in the blanks of prompts such as “Obama is a __ by profession”. These prompts are usually manually created, and quite possibly sub-optimal; another prompt such as “Obama worked as a __ ” may result in more accurately predicting the correct profession. Because of this, given an inappropriate prompt, we might fail to retrieve facts that the LM does know, and thus any given prompt only provides a lower bound estimate of the knowledge contained in an LM. In this paper, we attempt to more accurately estimate the knowledge contained in LMs by automatically discovering better prompts to use in this querying process. Specifically, we propose mining-based and paraphrasing-based methods to automatically generate high-quality and diverse prompts, as well as ensemble methods to combine answers from different prompts. Extensive experiments on the LAMA benchmark for extracting relational knowledge from LMs demonstrate that our methods can improve accuracy from 31.1% to 39.6%, providing a tighter lower bound on what LMs know. We have released the code and the resulting LM Prompt And Query Archive (LPAQA) at https://github.com/jzbjyb/LPAQA.


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Interoperable Annotation of Events and Event Relations across Domains
Jun Araki | Lamana Mulaffer | Arun Pandian | Yukari Yamakawa | Kemal Oflazer | Teruko Mitamura
Proceedings 14th Joint ACL - ISO Workshop on Interoperable Semantic Annotation

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Open-Domain Event Detection using Distant Supervision
Jun Araki | Teruko Mitamura
Proceedings of the 27th International Conference on Computational Linguistics

This paper introduces open-domain event detection, a new event detection paradigm to address issues of prior work on restricted domains and event annotation. The goal is to detect all kinds of events regardless of domains. Given the absence of training data, we propose a distant supervision method that is able to generate high-quality training data. Using a manually annotated event corpus as gold standard, our experiments show that despite no direct supervision, the model outperforms supervised models. This result indicates that the distant supervision enables robust event detection in various domains, while obviating the need for human annotation of events.


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Incorporating Relational Knowledge into Word Representations using Subspace Regularization
Abhishek Kumar | Jun Araki
Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)

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Generating Questions and Multiple-Choice Answers using Semantic Analysis of Texts
Jun Araki | Dheeraj Rajagopal | Sreecharan Sankaranarayanan | Susan Holm | Yukari Yamakawa | Teruko Mitamura
Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers

We present a novel approach to automated question generation that improves upon prior work both from a technology perspective and from an assessment perspective. Our system is aimed at engaging language learners by generating multiple-choice questions which utilize specific inference steps over multiple sentences, namely coreference resolution and paraphrase detection. The system also generates correct answers and semantically-motivated phrase-level distractors as answer choices. Evaluation by human annotators indicates that our approach requires a larger number of inference steps, which necessitate deeper semantic understanding of texts than a traditional single-sentence approach.


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Joint Event Trigger Identification and Event Coreference Resolution with Structured Perceptron
Jun Araki | Teruko Mitamura
Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing


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Evaluation for Partial Event Coreference
Jun Araki | Eduard Hovy | Teruko Mitamura
Proceedings of the Second Workshop on EVENTS: Definition, Detection, Coreference, and Representation

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Supervised Within-Document Event Coreference using Information Propagation
Zhengzhong Liu | Jun Araki | Eduard Hovy | Teruko Mitamura
Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC'14)

Event coreference is an important task for full text analysis. However, previous work uses a variety of approaches, sources and evaluation, making the literature confusing and the results incommensurate. We provide a description of the differences to facilitate future research. Second, we present a supervised method for event coreference resolution that uses a rich feature set and propagates information alternatively between events and their arguments, adapting appropriately for each type of argument.

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Detecting Subevent Structure for Event Coreference Resolution
Jun Araki | Zhengzhong Liu | Eduard Hovy | Teruko Mitamura
Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC'14)

In the task of event coreference resolution, recent work has shown the need to perform not only full coreference but also partial coreference of events. We show that subevents can form a particular hierarchical event structure. This paper examines a novel two-stage approach to finding and improving subevent structures. First, we introduce a multiclass logistic regression model that can detect subevent relations in addition to full coreference. Second, we propose a method to improve subevent structure based on subevent clusters detected by the model. Using a corpus in the Intelligence Community domain, we show that the method achieves over 3.2 BLANC F1 gain in detecting subevent relations against the logistic regression model.


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An NLP-based Reading Tool for Aiding Non-native English Readers
Mahmoud Azab | Ahmed Salama | Kemal Oflazer | Hideki Shima | Jun Araki | Teruko Mitamura
Proceedings of the International Conference Recent Advances in Natural Language Processing RANLP 2013

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Events are Not Simple: Identity, Non-Identity, and Quasi-Identity
Eduard Hovy | Teruko Mitamura | Felisa Verdejo | Jun Araki | Andrew Philpot
Workshop on Events: Definition, Detection, Coreference, and Representation

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An English Reading Tool as a NLP Showcase
Mahmoud Azab | Ahmed Salama | Kemal Oflazer | Hideki Shima | Jun Araki | Teruko Mitamura
The Companion Volume of the Proceedings of IJCNLP 2013: System Demonstrations