Takeshi Kutsumi

EFL learner reading time model for evaluating reading proficiency

We propose a reading time model for learners of English as a foreign language (EFL) that is based on a learners reading proficiency and the linguistic properties of sentences. Reading proficiency here refers to a learners reading score on the Test of English for International Communications (TOEIC), and the linguistic properties are the lexical, syntactic and discourse complexities of a sentence. We used natural language processing technology to automatically extract these linguistic properties, and developed a model using multiple regression analysis as a learning algorithm in combining the learners proficiency and linguistic properties. Experimental results showed that our reading time model predicted sentence-reading time with a 22.9% error rate, which is lower than the models constructed based on linguistic properties proposed in previous studies.

Validity of an Automatic Evaluation of Machine Translation Using a Word-Alignment-Based Classifier

Because human evaluation of machine translation is extensive but expensive, we often use automatic evaluation in developing a machine translation system. From viewpoint of evaluation cost, there are two types of evaluation methods: one uses (multiple) reference translation, e.g., METEOR, and the other classifies machine translation either into machine-like or human-like translation based on translation properties, i.e., a classification-based method. Previous studies showed that classification-based methods could perform evaluation properly. These studies constructed a classifier by learning linguistic properties of translation such as length of a sentence, syntactic complexity, and literal translation, and their classifiers marked high classification accuracy. These previous studies, however, have not examined whether their classification accuracy could present translation quality. Hence, we investigated whether classification accuracy depends on translation quality. The experiment results showed that our method could correctly distinguish the degrees of translation quality.

Expansion of machine translation bilingual dictionaries by using existing dictionaries and thesauruses

This paper gives a method of expanding bilingual dictionaries by creating a new multi-word entry (MWE) and its possible translation previously unregistered in bilingual dictionaries by replacing one of the components of a registered MWE with its semantically similar words, and then selecting appropriate lexical entries from the pairs of new MWEs and their possible translations according to a prioritizing method. In the proposed method, the pairs of new nominal MWEs and their possible translations are prioritized by referring to more than one thesaurus and considering the number of original MWEs from which a single new MWE is created. As a result, the pairs which are effective for improving translation quality, if registered in bilingual dictionaries, are acquired with an improvement of 55.0% for the top 500 prioritized pairs. This accuracy rate exceeds the one marked with the baseline method.

Selection of Dictionary Entries from Translation Equivalents for Acquisition of Lexical Knowledge from Bilingual Corpora

機械翻訳システムの翻訳品質を改善するためなどに必要な語彙知識を獲得するためには, 対訳コーパスにおいて二言語の表現を正しく対応付ける処理と, 対応付けられた表現対を辞書に登録するか否かを判定する選別処理の二つが必要である.従来, 対応付けに関する研究は数多く行なわれてきたが, 辞書登録候補の選別に関する研究はほとんど行なわれていない.本稿では, 従来あまり扱われてこなかった選別問題を採り上げ, この問題を機械学習によって解く方法を示す.学習に用いる素性として, 二つの表現の間で異なる部分と両者に共通する部分に着目し, 差分部分や共通部分を表現する手段として, 表記 (文字, 形態素), 品詞, 概念識別子を用いる.評価実験の結果, 最も高い選別性能 (F値) を示す表現方法は文字であることが明らかになった.

Acquisition of translation knowledge of syntactically ambiguous named entity

Bilingual dictionaries are essential components of cross-lingual information retrieval applications. The automatic acquisition of proper names and their translations from bilingual corpora is especially important, because a significant portion of the entries not listed in the dictionaries would be proper names.

Acquisition of Translation Knowledge of Syntactically Ambiguous Named Entity

本稿では, 前置詞句や等位構造を持つ英語固有表現とそれに対応する日本語表現を対訳コーパスから抽出する方法を提案する.提案方法では, (1) 意味的類似性と音韻的類似性の二つの観点から英語固有表現と日本語表現の対を評価し, 二種類の類似度を統合して全体としての類似度を求める処理と, (2) 前置詞句の係り先や等位構造の範囲が不適格である英語固有表現の抽出を抑制する処理を行なう.読売新聞とThe Daily Yomiuriの対訳コーパスを用いて実験を行ない, 提案方法の性能と上記のような処理を行なわないベースラインの性能を比較したところ, 提案方法で得られたF値0.678がベースラインでのF値0.583を上回り, 提案方法の有効性が示された.