United States

Text Simplification has been an extensively researched problem in English, but has not been investigated in Vietnamese. We focus on the Vietnamese-specific Complex Word Identification task, often the first step in Lexical Simplification (Shardlow, 2013). We examine three different Vietnamese datasets constructed for other Natural Language Processing tasks and show that, like in other languages, frequency is a strong signal in determining whether a word is complex, with a mean accuracy of 86.87%. Across the datasets, we find that the 10% most frequent words in many corpus can be labelled as simple, and the rest as complex, though this is more variable for smaller corpora. We also examine how human annotators perform at this task. Given the subjective nature, there is a fair amount of variability in which words are seen as difficult, though majority results are more consistent.

NAACL 2022

Complex Word Identification in Vietnamese: Towards Vietnamese Text Simplification

vietnamese

lexical simplification

frequency

text simplification

complex word identification

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IJCNLP-AACL 2021

The great event is jointly organized by the Association for Computational Linguistics (ACL) and Asian Federation of Natural Language Processing (AFNLP).

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## Welcome to NAACL 2022!
The Annual Meeting of the North American Chapter of the Association for Computational Linguistics (NAACL) is taking place July 10-15, 2022 as a hybrid event, in Seattle, WA and online. We are happy to welcome all of you to conference! 
 
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2022 Annual Conference of the North American Chapter of the Association for Computational Linguistics

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mLUKE: The Power of Entity Representations in Multilingual Pretrained Language Models

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"Training Text-to-Text Transformers with Privacy Guarantees"

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Pre-trained Language Models’ Interpretation of Evaluativity Implicature: Evidence from Gradable Adjectives Usage in Context

An intelligent system is expected to perform reasonable inferences, accounting for both the literal meaning of a word and the meanings a word can acquire in different contexts. A specific kind of inference concerns the connective and, which in some cases gives rise to a temporal succession or causal interpretation in contrast with the logic, commutative one (Levinson, 2000). In this work, we investigate the phenomenon by creating a new dataset for evaluating the interpretation of and by NLI systems, which we use to test three Transformer-based models. Our results show that all systems generalize patterns that are consistent with both the logical and the pragmatic interpretation, perform inferences that are inconsistent with each other, and show clear divergences with both theoretical accounts and humans’ behavior.

Pragmatic and Logical Inferences in NLI Systems: The Case of Conjunction Buttressing

Prior studies have raised concerns over specificity issues in clinical advice. Lacking specificity — explicitly discussed detailed information — may affect the quality and implementation of clinical advice in medical practice. In this study, we developed and validated a fine-grained annotation schema to describe differ- ent aspects of specificity in clinical advice extracted from the medical research literature. We also presented our initial annotation effort and discussed future directions towards an NLP- based specificity analysis tool for summarizing and verifying the details in clinical advice.


"Devils are in the Details'' — Annotating Specificity of Clinical Advice from Medical Literature

This paper presents a linguistically driven proof of concept for finding potentially euphemistic terms, or PETs. Acknowledging that PETs tend to be commonly used expressions for a certain range of sensitive topics, we make use of distributional similarities to select and filter phrase candidates from a sentence and rank them using a set of simple sentiment-based metrics. We present the results of our approach tested on a corpus of sentences containing euphemisms, demonstrating its efficacy for detecting single and multi-word PETs from a broad range of topics. We also discuss future potential for sentiment-based methods on this task.

Searching for PETs: Using Distributional and Sentiment-Based Methods to Find Potentially Euphemistic Terms

We report results of experiments using BART (Lewis et al., 2019) and the Penn Discourse Tree Bank (Webber et al., 2019) (PDTB) to generate texts with correctly realized discourse relations. We address a question left open by previous research (Yung et al., 2021; Ko and Li, 2020) concerning whether conditioning the model on the intended discourse relation—which corresponds to adding explicit discourse relation information into the input to the model—improves its performance. Our results suggest that including discourse relation information in the input of the model significantly improves the consistency with which it produces a correctly realized discourse relation in the output. We exemplify and argue for the integration of metrics inspired by hypotheses in psycholinguistics into our evaluation

Complex Word Identification in Vietnamese: Towards Vietnamese Text Simplification

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Benchmarking Language-agnostic Intent Classification for Virtual Assistant Platforms

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SemEval-2021 Task 1: Lexical Complexity Prediction

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Complex Word Identification in Vietnamese: Towards Vietnamese Text Simplification

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Next from NAACL 2022

Benchmarking Language-agnostic Intent Classification for Virtual Assistant Platforms

Similar lecture

SemEval-2021 Task 1: Lexical Complexity Prediction

Stay up to date with the latest Underline news!

PRESENTATIONS

CONFERENCES

COMPANY

RESOURCES

Downloads