| Challenge: | Existing methods to recommend quotes are evaluated on unpublished datasets . |
| Approach: | They propose to build a dataset that is open and contains three parts including English, standard Chinese and classical Chinese. |
| Outcome: | The proposed model outperforms existing methods on all three parts of QuoteR. |
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| Challenge: | Existing quotation recommendation system focuses on what to quote, but ignores whether or when to quote. |
| Approach: | They propose a framework that learns to predict when to quote and what to quote jointly. |
| Outcome: | The proposed framework achieves significantly better performance than baselines on two datasets. |
Who Said What: Formalization and Benchmarks for the Task of Quote Attribution (2024.lrec-main)
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| Challenge: | Existing methods for quote attribution are poorly understood, despite advances in research . previous approaches have used hand-crafted features to identify speaker names . |
| Approach: | They formalize the task of quote attribution and establish a basis for comparison . they compare CEQA and ChatGPT models on available datasets in both English and Chinese . |
| Outcome: | The proposed model outperforms all supervised methods on English and Chinese datasets. |
Content-based Models of Quotation (2021.eacl-main)
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| Challenge: | Prior work has focused on manual feature engineering and development of frameworks to test factors that influence quotability. |
| Approach: | They propose to use quotability identification as a passage ranking problem to evaluate models' performance . they use five datasets that span multiple languages and genres of literature . |
| Outcome: | The proposed model outperforms the existing model on five datasets that span multiple languages and genres of literature. |
DirectQuote: A Dataset for Direct Quotation Extraction and Attribution in News Articles (2022.lrec-1)
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| Challenge: | Existing methods to extract and attribute quotations from news data are difficult and require a lot of effort. |
| Approach: | They propose a corpus of 19,760 paragraphs and 10,279 direct quotations manually annotated from online news media. |
| Outcome: | The proposed corpus contains 19,760 paragraphs and 10,279 direct quotations manually annotated from online news media. |
Improving Automatic Quotation Attribution in Literary Novels (2023.acl-short)
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| Challenge: | Existing methods for quotation attribution in literary novels require varying levels of available information. |
| Approach: | They propose to train and evaluate models for character identification, coreference resolution, quotation identification and speaker attribution tasks using an annotated dataset. |
| Outcome: | The proposed model scores on speaker attribution task on the same scale as state-of-the-art models. |
Attribution, Citation, and Quotation: A Survey of Evidence-based Text Generation with Large Language Models (2026.acl-long)
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| Challenge: | Recent advances in large language models have raised concerns about reliability and trustworthiness of the models. |
| Approach: | They analyze 134 papers and introduce a taxonomy of evidence-based text generation with LLMs. |
| Outcome: | The proposed methods highlight open challenges and outline promising directions for future work. |
Automatic Argument Quality Assessment - New Datasets and Methods (D19-1)
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Assaf Toledo, Shai Gretz, Edo Cohen-Karlik, Roni Friedman, Elad Venezian, Dan Lahav, Michal Jacovi, Ranit Aharonov, Noam Slonim
| Challenge: | 6.3k arguments were collected from contributors of various levels, and are released as part of this work. |
| Approach: | They propose to use a language model to annotate arguments for argument ranking and argument-pair classification. |
| Outcome: | The proposed methods outperform state-of-the-art methods in the argument ranking task and argument-pair classification task. |
Continuity of Topic, Interaction, and Query: Learning to Quote in Online Conversations (2020.emnlp-main)
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| Challenge: | Quotations are crucial for successful explanations and persuasions in interpersonal communications. |
| Approach: | They propose to use an encoder-decoder neural framework to continue the context with a quotation via language generation to capture latent topics, interactions with the dialogue history, and coherence to the existing contents. |
| Outcome: | The proposed model outperforms state-of-the-art models on two large-scale datasets in English and Chinese and shows that topic, interaction, and query consistency are helpful to learn how to quote in online conversations. |
MoNoise: A Multi-lingual and Easy-to-use Lexical Normalization Tool (P19-3)
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| Challenge: | In this paper, we demonstrate the online demo and command line interface of a lexical normalization system (MoNoise) for a variety of languages. |
| Approach: | They propose to bundle seven datasets in six languages to form a new benchmark and a novel evaluation metric which is particularly suitable for cross-dataset comparisons. |
| Outcome: | The proposed model is based on the original word and features from the original language for each normalization candidate. |
Verifiable by Design: Aligning Language Models to Quote from Pre-Training Data (2025.naacl-long)
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| Challenge: | Recent efforts to verify text accuracy provide no guarantees on their correctness . a new method to improve LLMs' verifiability is to use quotes to ground models . |
| Approach: | They propose a method that allows models to quote verbatim statements from trusted sources . they leverage a fast membership inference function to verify text against trusted corpora . |
| Outcome: | The proposed method significantly increases verbatim quotes from high-quality documents by up to 130% relative to base models while maintaining response quality. |