A fundamental question in language learning concerns the role of a speaker's first language in second language acquisition. We present a novel methodology for studying this question: analysis of eye-movement patterns in second language reading of free-form text. Using this methodology, we demonstrate for the first time that the native language of English learners can be predicted from their gaze fixations when reading English. We provide analysis of classifier uncertainty and learned features, which indicates that differences in English reading are likely to be rooted in linguistic divergences across native languages. The presented framework complements production studies and offers new ground for advancing research on multilingualism.
Second language reading has been studied using eyetracking, with much of the work focusing on processing of syntactic ambiguities and analysis of specific target word classes such as cognates @cite_10 @cite_16 . In contrast to our work, such studies typically use controlled, rather than free-form sentences. Investigation of global metrics in free-form second language reading was introduced only recently by . This study compared ESL and native reading of a novel by native speakers of Dutch, observing longer sentence reading times, more fixations and shorter saccades in ESL reading. Differently from this study, our work focuses on comparison of reading patterns between different native languages. We also analyze a related, but different metric, namely speed normalized fixation durations on word sequences.
@cite_16 @cite_10
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When hearing or reading words and sentences in a second language (L2), we face many uncertainties about how the people and objects referred to are connected to one another. So what do we do under these conditions of uncertainty? Because relatively proficient L2 speakers have access to the grammar and lexicon of each language when comprehending words and sentences or when planning spoken utterances, and because the recent research suggests that these linguistic systems are not entirely independent, there is a critical question about how the knowledge of two languages affects basic aspects of language processing. In this article, I review how eye-tracking methodology has been used as a tool to address this question. I begin by discussing why eye movements are a useful methodology in language processing research, and I provide a description of one experimental paradigm developed to explore eye movements during reading. Second, I present recent developments in the use of eye tracking to study L2 spoken-language comprehension. I also highlight the importance of using multiple measures of online sentence processing by discussing results obtained using a moving window task and eye-tracking records while L2 speakers read syntactically ambiguous relative clauses. Next, I discuss research investigating syntactic processing when L2 speakers process mixed language. I end with suggestions for future research directions. Second language (L2) researchers are becoming more interested in both L2 learners’ knowledge of the target language and how that knowledge is put to use during real-time language processing. Researchers are therefore beginning to see the importance of combining traditional L2 research methods with those that capture the moment-by-moment interpretation of the target language, such as eye-tracking. The major benefi t of the eye-tracking method is that it can tap into real-time (or online) comprehension processes during the uninterrupted processing of the input, and thus, the data can be compared to those elicited by other, more met alinguistic tasks to offer a broader picture of language acquisition and processing. In this article, we present an overview of the eye-tracking technique and illustrate the method with L2 studies that show how eye-tracking data can be used to (a) investigate language-related topics and (b) inform key debates in the fi elds of L2 acquisition and L2 processing. This paper describes a system and set of algorithms for automatically inducing stand-alone monolingual part-of-speech taggers, base noun-phrase bracketers, named-entity taggers and morphological analyzers for an arbitrary foreign language. Case studies include French, Chinese, Czech and Spanish.Existing text analysis tools for English are applied to bilingual text corpora and their output projected onto the second language via statistically derived word alignments. Simple direct annotation projection is quite noisy, however, even with optimal alignments. Thus this paper presents noise-robust tagger, bracketer and lemmatizer training procedures capable of accurate system bootstrapping from noisy and incomplete initial projections.Performance of the induced stand-alone part-of-speech tagger applied to French achieves 96 core part-of-speech (POS) tag accuracy, and the corresponding induced noun-phrase bracketer exceeds 91 F-measure. The induced morphological analyzer achieves over 99 lemmatization accuracy on the complete French verbal system.This achievement is particularly noteworthy in that it required absolutely no hand-annotated training data in the given language, and virtually no language-specific knowledge or resources beyond raw text. Performance also significantly exceeds that obtained by direct annotation projection. Researchers in both machine translation (e.g., 1990) and bilingual lexicography (e.g., Klavans and Tzoukermann 1990) have recently become interested in studying bilingual corpora, bodies of text such as the Canadian Hansards (parliamentary proceedings), which are available in multiple languages (such as French and English). One useful step is to align the sentences, that is, to identify correspondences between sentences in one language and sentences in the other language.This paper will describe a method and a program (align) for aligning sentences based on a simple statistical model of character lengths. The program uses the fact that longer sentences in one language tend to be translated into longer sentences in the other language, and that shorter sentences tend to be translated into shorter sentences. A probabilistic score is assigned to each proposed correspondence of sentences, based on the scaled difference of lengths of the two sentences (in characters) and the variance of this difference. This probabilistic score is used in a dynamic programming framework to find the maximum likelihood alignment of sentences.It is remarkable that such a simple approach works as well as it does. An evaluation was performed based on a trilingual corpus of economic reports issued by the Union Bank of Switzerland (UBS) in English, French, and German. The method correctly aligned all but 4 of the sentences. Moreover, it is possible to extract a large subcorpus that has a much smaller error rate. By selecting the best-scoring 80 of the alignments, the error rate is reduced from 4 to 0.7 . There were more errors on the English-French subcorpus than on the English-German subcorpus, showing that error rates will depend on the corpus considered; however, both were small enough to hope that the method will be useful for many language pairs.To further research on bilingual corpora, a much larger sample of Canadian Hansards (approximately 90 million words, half in English and and half in French) has been aligned with the align program and will be available through the Data Collection Initiative of the Association for Computational Linguistics (ACL DCI). In addition, in order to facilitate replication of the align program, an appendix is provided with detailed c-code of the more difficult core of the align program.