Yo Ehara


2021

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Evaluation of Unsupervised Automatic Readability Assessors Using Rank Correlations
Yo Ehara
Proceedings of the 2nd Workshop on Evaluation and Comparison of NLP Systems

Automatic readability assessment (ARA) is the task of automatically assessing readability with little or no human supervision. ARA is essential for many second language acquisition applications to reduce the workload of annotators, who are usually language teachers. Previous unsupervised approaches manually searched textual features that correlated well with readability labels, such as perplexity scores of large language models. This paper argues that, to evaluate an assessors’ performance, rank-correlation coefficients should be used instead of Pearson’s correlation coefficient (𝜌). In the experiments, we show that its performance can be easily underestimated using Pearson’s 𝜌, which is significantly affected by the linearity of the output readability scores. We also propose a lightweight unsupervised readability assessor that achieved the best performance in both the rank correlations and Pearson’s 𝜌 among all unsupervised assessors compared.

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To What Extent Does Lexical Normalization Help English-as-a-Second Language Learners to Read Noisy English Texts?
Yo Ehara
Proceedings of the Seventh Workshop on Noisy User-generated Text (W-NUT 2021)

How difficult is it for English-as-a-second language (ESL) learners to read noisy English texts? Do ESL learners need lexical normalization to read noisy English texts? These questions may also affect community formation on social networking sites where differences can be attributed to ESL learners and native English speakers. However, few studies have addressed these questions. To this end, we built highly accurate readability assessors to evaluate the readability of texts for ESL learners. We then applied these assessors to noisy English texts to further assess the readability of the texts. The experimental results showed that although intermediate-level ESL learners can read most noisy English texts in the first place, lexical normalization significantly improves the readability of noisy English texts for ESL learners.

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Readability and Linearity
Yo Ehara
Proceedings of the 35th Pacific Asia Conference on Language, Information and Computation

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To What Extent Can English-as-a-Second Language Learners Read Economic News Texts?
Yo Ehara
Proceedings of the Third Workshop on Economics and Natural Language Processing

In decision making in the economic field, an especially important requirement is to rapidly understand news to absorb ever-changing economic situations. Given that most economic news is written in English, the ability to read such information without waiting for a translation is particularly valuable in economics in contrast to other fields. In consideration of this issue, this research investigated the extent to which non-native English speakers are able to read economic news to make decisions accordingly – an issue that has been rarely addressed in previous studies. Using an existing standard dataset as training data, we created a classifier that automatically evaluates the readability of text with high accuracy for English learners. Our assessment of the readability of an economic news corpus revealed that most news texts can be read by intermediate English learners. We also found that in some cases, readability varies considerably depending on the knowledge of certain words specific to the economic field.

2020

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Interpreting Neural CWI Classifiers’ Weights as Vocabulary Size
Yo Ehara
Proceedings of the Fifteenth Workshop on Innovative Use of NLP for Building Educational Applications

Complex Word Identification (CWI) is a task for the identification of words that are challenging for second-language learners to read. Even though the use of neural classifiers is now common in CWI, the interpretation of their parameters remains difficult. This paper analyzes neural CWI classifiers and shows that some of their parameters can be interpreted as vocabulary size. We present a novel formalization of vocabulary size measurement methods that are practiced in the applied linguistics field as a kind of neural classifier. We also contribute to building a novel dataset for validating vocabulary testing and readability via crowdsourcing.

2019

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An Approach to Summarize Concordancers’ Lists Visually to Support Language Learners in UnderstandingWord Usages
Yo Ehara
Proceedings of the 1st Workshop on Interactive Natural Language Technology for Explainable Artificial Intelligence (NL4XAI 2019)

2018

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Building an English Vocabulary Knowledge Dataset of Japanese English-as-a-Second-Language Learners Using Crowdsourcing
Yo Ehara
Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)

2017

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Language-Independent Prediction of Psycholinguistic Properties of Words
Yo Ehara
Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 2: Short Papers)

The psycholinguistic properties of words, namely, word familiarity, age of acquisition, concreteness, and imagery, have been reported to be effective for educational natural language-processing tasks. Previous studies on predicting the values of these properties rely on language-dependent features. This paper is the first to propose a practical language-independent method for predicting such values by using only a large raw corpus in a language. Through experiments, our method successfully predicted the values of these properties in two languages. The results for English were competitive with the reported accuracy achieved using features specific to English.

2016

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Generating Video Description using Sequence-to-sequence Model with Temporal Attention
Natsuda Laokulrat | Sang Phan | Noriki Nishida | Raphael Shu | Yo Ehara | Naoaki Okazaki | Yusuke Miyao | Hideki Nakayama
Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers

Automatic video description generation has recently been getting attention after rapid advancement in image caption generation. Automatically generating description for a video is more challenging than for an image due to its temporal dynamics of frames. Most of the work relied on Recurrent Neural Network (RNN) and recently attentional mechanisms have also been applied to make the model learn to focus on some frames of the video while generating each word in a describing sentence. In this paper, we focus on a sequence-to-sequence approach with temporal attention mechanism. We analyze and compare the results from different attention model configuration. By applying the temporal attention mechanism to the system, we can achieve a METEOR score of 0.310 on Microsoft Video Description dataset, which outperformed the state-of-the-art system so far.

2015

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Risk-aware distribution of SMT outputs for translation of documents targeting many anonymous readers
Yo Ehara | Masao Utiyama | Eiichiro Sumita
Proceedings of the 12th International Workshop on Spoken Language Translation: Papers

2014

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Formalizing Word Sampling for Vocabulary Prediction as Graph-based Active Learning
Yo Ehara | Yusuke Miyao | Hidekazu Oiwa | Issei Sato | Hiroshi Nakagawa
Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP)

2013

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Understanding seed selection in bootstrapping
Yo Ehara | Issei Sato | Hidekazu Oiwa | Hiroshi Nakagawa
Proceedings of TextGraphs-8 Graph-based Methods for Natural Language Processing

2012

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Mining Words in the Minds of Second Language Learners: Learner-Specific Word Difficulty
Yo Ehara | Issei Sato | Hidekazu Oiwa | Hiroshi Nakagawa
Proceedings of COLING 2012

2008

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Multilingual Text Entry using Automatic Language Detection
Yo Ehara | Kumiko Tanaka-Ishii
Proceedings of the Third International Joint Conference on Natural Language Processing: Volume-I