Sentence Simplification aims to make sentences easier to read and understand. With most effort on corpus development focused on English, the amount of annotated data is limited in Chinese. To address this need, we introduce CSSWiki, an open-source dataset for Chinese sentence simplification based on Wikipedia. This dataset contains 1.6k source sentences paired with their simplified versions. Each sentence pair is annotated with operation tags that distinguish between linguistic and content modifications. We analyze differences in annotation scheme and data statistics between CSSWiki and existing datasets. We then report baseline sentence simplification performance on CSSWiki using zero-shot and few-shot approaches with Large Language Models.

John Sie Yuen Lee, Ho Hung Lim, Carol Carol Webster. Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. 2022.

A nominalization uses a deverbal noun to describe an event associated with its underlying verb.Commonly found in academic and formal texts, nominalizations can be difficult to interpret because of ambiguous semantic relations between the deverbal noun and its arguments.Our goal is to interpret nominalizations by generating clausal paraphrases.We address compound nominalizations with both nominal and adjectival modifiers, as well as prepositional phrases.In evaluations on a number of unsupervised methods, we obtained the strongest performance by using a pre-trained contextualized language model to re-rank paraphrase candidates identified by a textual entailment model.

While bilingual corpora have been instrumental for machine translation, their utility for training translators has been less explored. We investigate the use of bilingual corpora as pedagogical tools for translation in the technical domain. In a user study, novice translators revised Chinese translations of English patents through bilingual concordancing. Results show that concordancing with an in-domain bilingual corpus can yield greater improvement in translation quality of technical terms than a general-domain bilingual corpus.

This paper describes a writing assistance system that helps students improve their academic writing by incorporating vocabulary that is more typical in the academic setting.Given an input text, the system suggests word substitutions according to an academic word list, and ranks them with a masked language model.Experimental results show that lexical formality analysis can improve the quality of the suggestions, in comparison to a baseline that relies on the masked language model only.Further, a user study demonstrate that students were able to use the system to improve text quality.

We present an annotation scheme and a dataset of teacher feedback provided for texts written by non-native speakers of English. The dataset consists of student-written sentences in their original and revised versions with teacher feedback provided for the errors. Feedback appears both in the form of open-ended comments and error category tags. We focus on a specific error type, namely linking adverbial (e.g. however, moreover) errors. The dataset has been annotated for two aspects: (i) revision outcome establishing whether the re-written student sentence was correct and (ii) directness, indicating whether teachers provided explicitly the correction in their feedback. This dataset allows for studies around the characteristics of teacher feedback and how these influence students’ revision outcome. We describe the data preparation process and we present initial statistical investigations regarding the effect of different feedback characteristics on revision outcome. These show that open-ended comments and mitigating expressions appear in a higher proportion of successful revisions than unsuccessful ones, while directness and metalinguistic terms have no effect. Given that the use of this type of data is relatively unexplored in natural language processing (NLP) applications, we also report some observations and challenges when working with feedback data.

We present the first approach to automatically building resources for academic writing. The aim is to build a writing aid system that automatically edits a text so that it better adheres to the academic style of writing. On top of existing academic resources, such as the Corpus of Contemporary American English (COCA) academic Word List, the New Academic Word List, and the Academic Collocation List, we also explore how to dynamically build such resources that would be used to automatically identify informal or non-academic words or phrases. The resources are compiled using different generic approaches that can be extended for different domains and languages. We describe the evaluation of resources with a system implementation. The system consists of an informal word identification (IWI), academic candidate paraphrase generation, and paraphrase ranking components. To generate candidates and rank them in context, we have used the PPDB and WordNet paraphrase resources. We use the Concepts in Context (CoInCO) “All-Words” lexical substitution dataset both for the informal word identification and paraphrase generation experiments. Our informal word identification component achieves an F-1 score of 82%, significantly outperforming a stratified classifier baseline. The main contribution of this work is a domain-independent methodology to build targeted resources for writing aids.

A lexical simplification (LS) system substitutes difficult words in a text with simpler ones to make it easier for the user to understand. In the typical LS pipeline, the Substitution Ranking step determines the best substitution out of a set of candidates. Most current systems do not consider the user's vocabulary proficiency, and always aim for the simplest candidate. This approach may overlook less-simple candidates that the user can understand, and that are semantically closer to the original word. We propose a personalized approach for Substitution Ranking to identify the candidate that is the closest synonym and is non-complex for the user. In experiments on learners of English at different proficiency levels, we show that this approach enhances the semantic faithfulness of the output, at the cost of a relatively small increase in the number of complex words.

A lexical simplification (LS) system aims to substitute complex words with simple words in a text, while preserving its meaning and grammaticality. Despite individual users’ differences in vocabulary knowledge, current systems do not consider these variations; rather, they are trained to find one optimal substitution or ranked list of substitutions for all users. We evaluate the performance of a state-of-the-art LS system on individual learners of English at different proficiency levels, and measure the benefits of using complex word identification (CWI) models to personalize the system. Experimental results show that even a simple personalized CWI model, based on graded vocabulary lists, can help the system avoid some unnecessary simplifications and produce more readable output.

This paper describes a personalized text retrieval algorithm that helps language learners select the most suitable reading material in terms of vocabulary complexity. The user first rates their knowledge of a small set of words, chosen by a graph-based active learning model. The system trains a complex word identification model on this set, and then applies the model to find texts that contain the desired proportion of new, challenging, and familiar vocabulary. In an evaluation on learners of Chinese as a foreign language, we show that this algorithm is effective in identifying simpler texts for low-proficiency learners, and more challenging ones for high-proficiency learners.

While a wide variety of grammatical mistakes may be observed in the speech of non-native speakers, the types and frequencies of these mistakes are not random. Certain parts of speech, for example, have been shown to be especially problematic for Japanese learners of English [1]. Modeling these errors can potentially enhance the performance of computer-assisted language learning systems. This paper presents an automatic method to estimate an error model from a non-native English corpus, focusing on articles and prepositions. A fine-grained analysis is achieved by conditioning the errors on appropriate words in the context.

Fill-in-the-blank questions, or cloze items, are commonly used in language learning applications. The benefits of personalized items, tailored to the user’s interest and proficiency, have motivated research on automatic generation of cloze items. This paper is concerned with generating cloze items for prepositions, whose usage often poses problems for non-native speakers of English. The quality of a cloze item depends on the choice of distractors. We propose two methods, based on collocations and on non-native English corpora, to generate distractors for prepositions. Both methods are found to be more successful in attracting users than a baseline that relies only on word frequency, a common criterion in past research. Index Terms: computer-assisted language learning, natural language generation

Speech utterance classification has been widely applied to a variety of spoken language understanding tasks, including call routing, dialog systems, and command and control. Most speech utterance classification systems adopt a data-driven statistical learning approach, which requires manually transcribed and annotated training data. In this paper we introduce a novel classification model training approach based on unsupervised language model adaptation. It only requires wave files of the training speech utterances and their corresponding classification destinations for modeling training. No manual transcription of the utterances is necessary. Experimental results show that this approach, which is much cheaper to implement, has achieved classification accuracy at the same level as the model trained with manual transcriptions.

A computer conversational system can potentially help a foreign-language student improve his/her fluency through practice dialogues. One of its potential roles could be to correct ungrammatical sentences. This paper 1 describes our research on a sentence-level, generation-based approach to grammar correction: first, a word lattice of candidate corrections is generated from an ill-formed input. A traditional n-gram language model is used to produce a small set of N-best candidates, which are then reranked by parsing using a stochastic context-free grammar. We evaluate this approach in a flight domain with simulated ill-formed sentences. We discuss its potential applications in a few related tasks. Index Terms: computer-assisted language learning, dialogue systems, natural language generation.

We propose an induction algorithm to semi-automate grammar authoring in an interlingua-based machine translation framework. This algorithm uses a pre-existing one-way translation system from some other language to the target language as prior information to infer a grammar for the target language. We demonstrate the system’s effectiveness by automatically inducing a Chinese grammar for a weather domain from its English counterpart, and showing that it can produce high-quality translation from Chinese back to English.