Negation Scope Resolution for C hinese as a Second Language (2021)

Abstract: This paper studies Negation Scope Resolution (NSR) for Chinese as a Second Language (CSL), which shows many unique characteristics that distinguish itself from “standard” Chinese. We annotate a new moderate-sized corpus that covers two background L1 languages, viz. English and Japanese. We build a neural NSR system, which achieves a new state-of-the-art accuracy on English benchmark data. We leverage this system to gauge how successful NSR for CSL can be. Different native language backgrounds of language learners result in unequal cross-lingual transfer, which has a significant impact on processing second language data. In particular, manual annotation, empirical evaluation and error analysis indicate two non-obvious facts: 1) L2-Chinese, L1-Japanese data are more difficult to analyze and thus annotate than L2-Chinese, L1-English data; 2) computational models trained on L2-Chinese, L1-Japanese data perform better than models trained on L2-Chinese, L1-English data.

## introduction in an increasingly globalized world, non-native speakers and writers all over the world produce a huge amount of second language (l2) data every day. there is naturally a need to automatically annotate such large-scale atypical data with rich lexical, syntactic, semantic and even pragmatic information. high-performance automatic annotation of such data, from an engineering perspective, enables deriving high-quality information by structuring this specific type of data, and from a scientific perspective, enables quantitative studies for second language acquisition (sla), which is complementary to hands-on experiences in interpreting second language phenomena @xcite . it is insufficient to directly apply models designed for first languages to handle the second language data because a second language is a standalone linguistic system @xcite that distinguishes itself from both the source and target languages in linguistic features. we need second language-specific methodologies by taking its characteristics into account. this direction has been recently explored, including syntactic and semantic parsing for english as a second language @xcite @xcite and semantic role labeling for chinese as a second language (hereafter l2-chinese or l2-chi for short) @xcite . negation is a ubiquitous phenomenon in spoken text (27.6 per 1000 words) and in written text (12.8 per 1000 words) @xcite . as a type of extra-propositional semantics, negation is relevant to analyze factuality of propositions and thus relevant to accurately understand natural languages which has been proven useful for several nlp applications such as sentiment analysis @xcite , machine reading comprehension @xcite , automatic question and answer @xcite , etc. in the nlp community, the morpheme or word that expresses negation is called the negation cue, which can be a single word or multiple words. a negation is related to an event, i.e. so-called negation event, which is identified by some keys words, like need in (1). if we want to know the complete information conveyed by a negation event such as what is not needed in (1), we need to identify a scope in the sentence, which is a set of words. to be more precise, the negation scope is the maximum part(s) of the sentence that are influenced or negated by negation cue. in (1), the negation scopes are marked with square brackets. in this paper, we study negation scope resolution (nsr) for l2-chinese, the goal of which is to automatically identify the scope of a negation cue. to the best of our knowledge, this is the first study on nsr for a second language. we build a new moderate-sized chinese corpus that contains manual negation annotations for 8000 sentences written by non-native speakers, as well as their corrections by native speakers. a high interannotator agreement is achieved, suggesting the robustness of language comprehension of csl. all annotated sentences are standard chinese or l2-chinese. we consider two typologically distant languages, i.e. english and japanese, as background native languages of language learners. we use l1-english (l1-eng for short) and l1-japanese (l1-jpn for short) to denote them respectively foot_0 . we also consider the corrected sentences by chinese native speakers and use "chi l2⇒l1 foot_1 , l1-eng" and "chi l2⇒l1 , l1-jpn" to denote them. the following are an example of l2-chi, l1-jpn (2a) as well as its correction (2b). ## the neural nsr model in this paper, we focus on the state-of-the-art solution that leverages on neural word-based tagging to solve the problem. the overview of our neural model is shown in fig. 1 . formally, our task is to predict a sequence y given a word-pos-cue pair w, p, c as input. each y @xmath0. a sentence may include more than one negation cues. to make sure the neural model is aware of which negation cue is in question, the value of c @xmath1, and 0 otherwise. as fig. 1 shows, we use bert @xcite or elmo @xcite or randomly initialized feedforward layers for word embedding of w i , randomly initialized feedforward layers for pos embedding of p i , randomly initialized feedforward layers for cue embedding of c i . then we use feed forward layers to compress word embedding vectors of bert and elmo to a lower dimension. the concatenation of feature embeddings is fed to an encoder (e.g., bilstm). the encoder's outputs then go through feedforward lay-c. f 1 s. f 1 t. f 1 c. kappa s. kappa cd-sco @xcite 94.88 85.04 91.53 --sfu review @xcite 92.79 81.88 -92.70 87.20 bioscope @xcite 98.65 95.91 ---cnesp ( zou et al., 2015 ) ---95.00 93.00 l2-chi, l1-eng 100.00 92.55 97.12 100.00 96.13 chi l2⇒l1 , l1-eng 100.00 92.55 97.71 100.00 95.65 l2-chi, l1-jpn 100.00 90.09 94.62 100.00 92.15 chi l2⇒l1 , l1-jpn 100.00 90.09 94.35 100.00 92.32 5 experiments ## conclusion in this paper, we construct a chinese negation corpus with sentences written by english, japanese and chinese native speakers respectively. compared to previous negation corpus, our corpus has several highlights: (1) our corpus has the largest scale and higher inter-annotator agreement; (2) our corpus is the first corpus that focuses on second lan-guage and contributes parallel sentences. also, we present a state of the art system for automatic nsr on the english benchmark data. we evaluate our system on our corpus on cross-language training scenarios where a model is trained and tested on different sub-corpora. results and error analysis show that cross-lingual transfer has a significant impact, but not on all languages equally. the observation that different first language background will have a different impact on the second language acquisition will give inspirations on nlp for second languages.