Challenge: Analyzing historical languages is challenging because they lack primary material for certain time periods . under-resourced languages such as Ancient Greek and Latin lack advanced natural-language processing (NLP) techniques .
Approach: They propose to use machine learning to detect and classify paraphrastic text reuse in historical texts.
Outcome: The proposed method improves the accuracy of paraphrastic text reuse detection in historical languages.

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Paraphrases as Foreign Languages in Multilingual Neural Machine Translation (P19-2)

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Challenge: Unlike previous studies that use paraphrases at the word/phrase level, we train on parallel paraphrase training on closely related languages.
Approach: They train on parallel paraphrases in the style of multilingual Neural Machine Translation (NMT) they train on translations of the whole corpus that are consistent in structure as paraphrase versions at the corpus level.
Outcome: The proposed training on paraphrases outperforms the baselines on two languages and improves lexical choice and entropy.
Evaluating Historical Text Normalization Systems: How Well Do They Generalize? (N18-2)

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Challenge: Historical text normalization systems aim to convert historical wordforms to their modern equivalents . many of these systems have been developed and tested on a single language .
Approach: They propose to use a nave baseline system to evaluate historical text normalization systems . they show that the models generalize well to unseen words in tests on five languages .
Outcome: The proposed models generalize well to unseen words on five languages, but provide no clear benefit over the nave baseline.
Diachronic word embeddings and semantic shifts: a survey (C18-1)

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Challenge: Existing methods for tracing time-related semantic shifts with word embedding models lack the cohesion, common terminology and shared practices of more established areas of natural language processing.
Approach: They propose several axes along which these methods can be compared and propose a framework for comparison.
Outcome: The proposed methods are compared with existing methods and outline their main challenges and potential applications.
Comparative Study of Sentence Embeddings for Contextual Paraphrasing (2020.lrec-1)

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Challenge: Paraphrasing is an important aspect of natural-language generation that can produce more variety in the way specific content is presented.
Approach: They propose to use contextual paraphrasing to capture the meaning of a sentence while performing dialogue act clustering.
Outcome: The proposed task combines paraphrases with dialogue act clustering to capture such contextual paraphrasing.
Evaluating Paraphrastic Robustness in Textual Entailment Models (2023.acl-short)

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Challenge: Recognizing Textual Entailment models understand language and should be robust to paraphrases.
Approach: They propose to evaluate whether RTE models are robust to paraphrase . they use 1,126 pairs of Recognizing Textual Entailment (RTE) examples to evaluate their models .
Outcome: The evaluation set shows that models change predictions on 8-16% of paraphrased examples, suggesting that there is room for improvement.
Paraphrase Types for Generation and Detection (2023.emnlp-main)

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Challenge: Current approaches to paraphrase generation and detection ignore the intricate linguistic properties of language.
Approach: They propose two tasks to consider specific linguistic perturbations at particular text positions.
Outcome: The proposed tasks address the shortcoming of ignoring the linguistic properties of language.
A Large-Scale Comparison of Historical Text Normalization Systems (N19-1)

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Challenge: a large study of historical text normalization is done on eight languages . there is no consensus on the state-of-the-art approach to normalization .
Approach: They present a large study of historical text normalization done on eight languages . they evaluate four different systems based on supervised learning on datasets from eight different languages based in the literature .
Outcome: The proposed methods are based on supervised learning and are available online.
Learning Cross-lingual Distributed Logical Representations for Semantic Parsing (P18-2)

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Challenge: Recent research efforts have looked into the problem of learning semantic parsers in a multilingual setup, but how to improve the performance of a monolingual semantic parsed system remains a research question that is under-explored.
Approach: They propose to use data annotated in different languages to learn distributed representations of logical forms for improving a monolingual semantic parser.
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A large-scale computational study of content preservation measures for text style transfer and paraphrase generation (2022.acl-srw)

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Challenge: Text style transfer and paraphrases generation are growing areas of NLP . many researchers still use BLEU-like measures to evaluate content preservation .
Approach: They compare 57 different measures based on different principles on 19 annotated datasets . they find that measures relying on cross-encoder models outperform alternative approaches .
Outcome: The proposed methods outperform traditional methods on 19 datasets.
Simple, Interpretable and Stable Method for Detecting Words with Usage Change across Corpora (2020.acl-main)

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Challenge: comparing two corpus texts and searching for words that differ in their usage between them is a common problem in digital humanities and computational social science.
Approach: They propose an alternative approach that does not use vector space alignment, and instead considers the neighbors of each word.
Outcome: The proposed method is interpretable and stable in 9 different setups and is highly reliable.

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