Papers with lexical substitution
Chinese Lexical Substitution: Dataset and Method (2023.emnlp-main)
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| Challenge: | Existing benchmarks for lexical substitution (LS) are limited and limited in coverage . despite extensive research on Lexical Substitution in various languages, there is limited evidence for LS in Chinese. |
| Approach: | They propose to use human and machine collaboration to construct a Chinese LS dataset . they combine four unsupervised LS methods to generate candidate substitutes . |
| Outcome: | The proposed method outperforms existing benchmarks on the Chinese lexical substitution task. |
TS-ANNO: An Annotation Tool to Build, Annotate and Evaluate Text Simplification Corpora (2022.acl-demo)
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| Challenge: | Currently, high-quality corpora of this type are rare and often of comparably small size. |
| Approach: | They propose an open-source web application for automatic text simplification. |
| Outcome: | TS-ANNO can be used for i) sentence–wise alignment, ii) rating alignment pairs, w.r.t. simplification transformations, and iv) manual simplification of complex documents. |
Near-imperceptible Neural Linguistic Steganography via Self-Adjusting Arithmetic Coding (2020.emnlp-main)
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| Challenge: | Linguistic steganography studies how to hide secret messages in natural language cover texts. |
| Approach: | They propose a method which encodes secret messages using self-adjusting arithmetic coding based on a neural language model. |
| Outcome: | The proposed method outperforms the state-of-the-art methods on four datasets by 15.3% and 38.9% in terms of bits/word and KL metrics. |
Edit Distance Based Curriculum Learning for Paraphrase Generation (2021.acl-srw)
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| Challenge: | Existing studies show that curriculum learning improves translation quality on machine translation . paraphrase generation allows a certain level of semantic divergence between source and target . |
| Approach: | They propose to apply curriculum learning to paraphrase generation for the first time . they propose to use edit distance to improve paraphrase quality . |
| Outcome: | The proposed method improves paraphrase generation quality, compared with previous methods . it uses edit distance, which is not possible for previous methods, the authors say . |
Lexical Substitution for Evaluating Compositional Distributional Models (N18-2)
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| Challenge: | Compositional Distributional Semantic Models (CDSMs) model the meaning of phrases and sentences in vector space. |
| Approach: | They propose to use lexical substitution to evaluate CDSMs by comparing a LexSub-annotated corpus with a manual LexSub annotation. |
| Outcome: | The proposed model outperforms simple component-wise CDSMs and performs on par with the context2vec LexSub model using the same context. |
Contextualized context2vec (D19-55)
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| Challenge: | Lexical substitution ranks substitution candidates from the viewpoint of paraphrasability for a target word in a given sentence. |
| Approach: | They propose a method that combines two approaches to contextualize word embeddings for lexical substitution. |
| Outcome: | The proposed method outperforms the current state-of-the-art method and assigns English proficiency levels to all target words and substitution candidates. |
Assessing the Representations of Idiomaticity in Vector Models with a Noun Compound Dataset Labeled at Type and Token Levels (2021.acl-long)
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| Challenge: | Existing resources for idiomaticity annotation only include ratings at type level . idioms such as noun compounds have been considered a challenge for NLP . |
| Approach: | They present a dataset with human annotations for 280 noun compounds in English and 180 in Portuguese at both type and token levels. |
| Outcome: | The proposed dataset shows that human annotations are not capturing idiomaticity as human annotation models. |
Unsupervised Rewriter for Multi-Sentence Compression (P19-1)
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| Challenge: | Multi-sentence compression aims to generate a grammatical but reduced compression from multiple input sentences while retaining key information. |
| Approach: | They propose a neural rewriter for multi-sentence compression that does not need any parallel corpus. |
| Outcome: | Empirical studies show that the proposed approach achieves comparable results upon automatic evaluation and improves the grammaticality of compression based on human evaluation. |
BERT-based Lexical Substitution (P19-1)
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| Challenge: | Existing approaches to lexical substitution tend to overlook good substitute candidates that are not the synonyms of the target words in the lexicals and fail to take into account the substitution’s influence on the global context of the sentence. |
| Approach: | They propose an end-to-end BERT-based lexical substitution approach which proposes and validates substitute candidates without using annotated data or manually curated resources. |
| Outcome: | The proposed approach performs well in proposing and ranking substitute candidates, achieving the state-of-the-art results in both LS07 and LS14 benchmarks. |
Swords: A Benchmark for Lexical Substitution with Improved Data Coverage and Quality (2021.naacl-main)
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| Challenge: | Existing benchmarks for lexical substitution depend on human recall as the only source of data, authors say . existing benchmarks lack coverage of the appropriate substitutes that would be most helpful to humans . |
| Approach: | They propose a benchmark for lexical substitution to find appropriate substitutes for a target word in context . existing benchmarks depend on human recall as the only source of data, they argue . |
| Outcome: | The new benchmark for lexical substitution uses a context-free thesaurus . it has 3x as many substitutes per target word for the same quality, and substitutes are 1.4x more appropriate . |
CILex: An Investigation of Context Information for Lexical Substitution Methods (2022.coling-1)
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| Challenge: | Existing methods for lexical substitution rely on manually curated lexicals and contextual word embedding models. |
| Approach: | They propose a method that uses contextual sentence embeddings to generate substitutes for a target word given a context and a model that captures additional context information complimenting contextual word embedders. |
| Outcome: | The proposed method is state-of-the-art on the widely used LS07 and CoInCo datasets with P@1 scores of 55.96% and 57.25% for lexical substitution. |
Unsupervised Lexical Substitution with Decontextualised Embeddings (2022.coling-1)
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| Challenge: | Existing methods for lexical substitution using pre-trained language models have some limitations. |
| Approach: | They propose an unsupervised method for lexical substitution using pre-trained language models. |
| Outcome: | The proposed method outperforms baseline models and establishes a state-of-the-art without supervision or fine-tuning. |
GeneSis: A Generative Approach to Substitutes in Context (2021.emnlp-main)
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| Challenge: | lexical substitution tasks require a system to provide adequate replacements for a word in a given context. |
| Approach: | They propose a generative approach to lexical substitution using a seq2seq model to generate suitable replacements for a word in context. |
| Outcome: | The proposed approach achieves state-of-the-art on different benchmarks and human evaluation of the generated substitutes. |