Papers with Wikipedia corpus
Dependency Patterns of Complex Sentences and Semantic Disambiguation for Abstract Meaning Representation Parsing (2021.starsem-1)
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| Challenge: | Abstract Meaning Representation (AMR) is a sentence-level meaning representation based on predicate argument structure. |
| Approach: | They propose to use a dictionary to capture the structure of complex sentences . they train models on data derived from AMR and Wikipedia corpus . |
| Outcome: | The proposed model will be made public and the proposed patterns will be validated. |
Decontextualization: Making Sentences Stand-Alone (2021.tacl-1)
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| Challenge: | Taking excerpts of text can be problematic, as key pieces may not be explicit in a local window. |
| Approach: | They define a problem of sentence decontextualization by rewriting a sentence to be interpretable out of context while preserving its meaning. |
| Outcome: | The proposed method can be used in question answering and document understanding tasks. |
SportReason: Evaluating Retrieval-Augmented Reasoning across Tables and Text for Sports Question Answering (2025.emnlp-main)
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| Challenge: | Existing benchmarks for retrieval-augmented reasoning on numerical sports questions focus on one or two evidence units. |
| Approach: | They propose a benchmark for retrieval-augmented reasoning on numerical sports questions . they evaluate existing retrievers and rerankers, along with agentic Retrieval-Augmented Generation systems. |
| Outcome: | The proposed benchmarks focus on the sports domain because it offers rich multi-modal resources. |
Capturing the Relationship Between Sentence Triplets for LLM and Human-Generated Texts to Enhance Sentence Embeddings (2024.findings-eacl)
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| Challenge: | Recent advances in building sentence embedding models have centered on replacing traditional human-generated text datasets with those generated by LLMs. |
| Approach: | They propose a loss function that incorporates Positive-Negative sample Augmentation within the contrastive learning objective to enhance sentence embeddings using both human and LLM-generated datasets. |
| Outcome: | The proposed model mitigates the sentence anisotropy problem in Wikipedia corpus and improves Spearman’s correlation in standard Semantic Textual Similarity (STS) tasks (+1.47% compared to CLHAIF). |
Men Are Elected, Women Are Married: Events Gender Bias on Wikipedia (2021.acl-short)
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| Challenge: | Disproportional event distributions can manifest and amplify social stereotypes . researchers have been using NLP tools to analyze corpora for various tasks on online platforms. |
| Approach: | They propose to scrape a corpus of career and personal life descriptions with demographic information from 10,412 celebrities to facilitate the study. |
| Outcome: | The proposed model detects gender biases in a corpus of career and personal life descriptions and calibrates the results using strategically generated templates. |
Made of Steel? Learning Plausible Materials for Components in the Vehicle Repair Domain (2023.eacl-main)
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| Challenge: | a novel approach to learn domain-specific plausible materials for components in the vehicle repair domain is proposed . connecting a symptom to an underlying cause is a crucial building block for natural language understanding across domains. |
| Approach: | They propose a method to aggregate salient predictions from a set of cloze task style templates and use a Wikipedia corpus to augment the model. |
| Outcome: | The proposed approach outperforms a traditional pattern-based approach by exploiting the compositionality assumption in a cloze task style setting. |
On the Helpfulness of Document Context to Sentence Simplification (2020.coling-main)
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| Challenge: | Text simplification is a hot issue in the field of natural language generation (NLG). |
| Approach: | They propose to use Wikipedia context to improve sentence simplification by using neural networks to learn the effects of preceding and following sentences on current sentences. |
| Outcome: | The proposed model outperforms the best performing model on the baseline dataset by 2.46 (7.22%). |
Llama2Vec: Unsupervised Adaptation of Large Language Models for Dense Retrieval (2024.acl-long)
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| Challenge: | Dense retrieval requires discriminative embeddings to represent the semantic relationship between query and document. |
| Approach: | They propose an unsupervised approach that performs unsupervised adaptation of large language models for dense retrieval. |
| Outcome: | The proposed model improves on a variety of dense retrieval benchmarks and is available on github. |
A Crowdsourced Frame Disambiguation Corpus with Ambiguity (N19-1)
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| Challenge: | Using crowdsourcing, we have found that inter-annotator disagreement is at least partly caused by ambiguity inherent to the text and frames. |
| Approach: | They propose a crowdsourcing approach to capture inter-annotator disagreement by a list of frames with disagreement-based scores that express the confidence with which each frame applies to the word. |
| Outcome: | The proposed approach captures disagreement between the annotations of 1,000 word-sentence pairs and scores on the likelihood that each frame applies to the word. |
RobustQA: Benchmarking the Robustness of Domain Adaptation for Open-Domain Question Answering (2023.findings-acl)
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Rujun Han, Peng Qi, Yuhao Zhang, Lan Liu, Juliette Burger, William Yang Wang, Zhiheng Huang, Bing Xiang, Dan Roth
| Challenge: | Existing ODQA datasets consist mainly of Wikipedia corpus, and are insufficient to study models’ generalizability across diverse domains. |
| Approach: | They propose a benchmark to evaluate ODQA's domain robustness using Wikipedia corpus . they annotate QA pairs in retrieval datasets with rigorous quality control . |
| Outcome: | The proposed benchmark improves model performance on annotated QA pairs in retrieval datasets with rigorous quality control. |
MIsA: Multilingual “IsA” Extraction from Corpora (L18-1)
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| Challenge: | In this paper, we present a collection of hypernymy relations extracted from the Wikipedia corpus in five languages. |
| Approach: | They present a collection of hypernymy relations extracted from the Wikipedia corpus in five languages . they use existing or newly defined lexico-syntactic patterns to extract hyperniyms . |
| Outcome: | The proposed tool is based on a dictionary extracted from the full Wikipedia corpus. |
Disentangling Transformer Language Models as Superposed Topic Models (2023.emnlp-main)
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| Challenge: | Topic Modelling is an established area of research where the quality of a given topic is measured using coherence metrics. |
| Approach: | They propose a weight-based approach to search and disentangle decoder-only TLM by a Wikipedia corpus. |
| Outcome: | The proposed approach can be applied to GPT-2 models and LLaMA models. |
Learn to Not Link: Exploring NIL Prediction in Entity Linking (2023.findings-acl)
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| Challenge: | Entity linking models have been successful in capturing semantic features, but the NIL prediction problem has not been addressed. |
| Approach: | They propose an entity linking dataset that categorizes mentions linking to NIL into Missing Entity and Non-Entity Phrases. |
| Outcome: | The proposed dataset categorizes mentions linking to NIL into Missing Entity and Non-Entity Phrase categories and ensures the presence of mentions by human annotation and entity masking. |
Italian Word Embeddings for the Medical Domain (2024.lrec-main)
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| Challenge: | Neural word embeddings have proven valuable in the development of medical applications, but for the Italian language, there are no publicly available corpora, embedds, or evaluation resources tailored to this domain. |
| Approach: | They propose to use a corpus of medical texts to generate neural word embeddings in Italian using Metathesaurus concept graphs. |
| Outcome: | The results show that the new embeddings correlate well with human judgments regarding similarity and relatedness of medical concepts. |