Papers by Matthew Shardlow
Detecting Multiword Expression Type Helps Lexical Complexity Assessment (2020.lrec-1)
Copied to clipboard
| Challenge: | Multiword expressions (MWEs) represent lexemes that should be treated as single lexical units due to their idiosyncratic nature. |
| Approach: | They re-annotate a complex word identification shared task 2018 dataset . they find that a lexical complexity assessment system benefits from the information . |
| Outcome: | The proposed dataset provides valuable information for the text simplification community. |
Neural Text Simplification of Clinical Letters with a Domain Specific Phrase Table (P19-1)
Copied to clipboard
| Challenge: | Clinical letters are written by doctors and typically contain complex medical language that is beyond the scope of the lay reader. |
| Approach: | They propose to augment existing neural text simplification software with a phrase table that links medical terminology to simpler vocabulary by mining SNOMED-CT data. |
| Outcome: | The proposed system is easier to understand than the existing system and the phrase table without it. |
CombiNMT: An Exploration into Neural Text Simplification Models (2020.lrec-1)
Copied to clipboard
| Challenge: | Neural Text Simplification (NMT) is a widely used technique in Machine Translation (NLP) |
| Approach: | They present a replication study of Exploring Neural Text Simplification Models using OpenNMT and Newsela datasets. |
| Outcome: | The proposed systems improve on the original paper by using an updated implementation of OpenNMT and the newsela corpus alongside the original Wikipedia dataset. |
A New Corpus to Support Text Mining for the Curation of Metabolites in the ChEBI Database (L18-1)
Copied to clipboard
Matthew Shardlow, Nhung Nguyen, Gareth Owen, Claire O’Donovan, Andrew Leach, John McNaught, Steve Turner, Sophia Ananiadou
| Challenge: | a corpus of 200 abstracts and 100 full text papers which have been annotated with named entities and relations in the biomedical domain is part of the OpenMinTeD project. |
| Approach: | They propose to annotate 200 abstracts and 100 full text papers with entities and relations in the biomedical domain as part of the OpenMinTeD project. |
| Outcome: | The proposed corpus can be used within ChEBI to facilitate text and data mining and integrate with the OpenMinTeD text and database platform. |
Investigating Text Simplification Evaluation (2021.findings-acl)
Copied to clipboard
| Challenge: | Existing studies show that parallel TS corpora contain inaccurate simplifications and incorrect alignments. |
| Approach: | They propose to improve the distribution of parallel text simplification corpora to build more robust TS models. |
| Outcome: | The proposed models can be improved by improving the distribution of TS datasets. |
Using NLP to quantify the environmental cost and diversity benefits of in-person NLP conferences (2022.findings-acl)
Copied to clipboard
| Challenge: | Figure 1 shows the increase in travel to the ACL annual meeting over the past 40 years . |
| Approach: | They analyse the carbon cost associated with journeys made by researchers attending in-person NLP conferences by text-mining publications from the ACL anthology . |
| Outcome: | The proposed model compares the carbon cost associated with travel to in-person conferences to previously known values for training large models. |
Exploring Supervised Approaches to the Detection of Anthropomorphic Language in the Reporting of NLP Venues (2025.findings-acl)
Copied to clipboard
| Challenge: | anthropomorphisms are used to describe technical contributions to AI . however, they also give potential for incorrect assumptions about LLMs' capacities. |
| Approach: | They undertake a corpus annotation of one year of ACL abstracts and news articles from the same period and train a regression classifier based on BERT to identify anthropomorphic language. |
| Outcome: | The proposed method can automatically label abstracts for their degree of anthropomorphism based on their corpus and reporting on diachronic and inter-venue findings. |
BLESS: Benchmarking Large Language Models on Sentence Simplification (2023.emnlp-main)
Copied to clipboard
Tannon Kew, Alison Chi, Laura Vásquez-Rodríguez, Sweta Agrawal, Dennis Aumiller, Fernando Alva-Manchego, Matthew Shardlow
| Challenge: | BLESS is a performance benchmark of the most recent state-of-the-art Large Language Models (LLMs) on the task of text simplification (TS). |
| Approach: | They present a performance benchmark of the most recent state-of-the-art Large Language Models (LLMs) on the task of text simplification (TS). |
| Outcome: | The proposed benchmarks show that the most recent state-of-the-art LLMs perform better on the task of text simplification (TS). |
Simple TICO-19: A Dataset for Joint Translation and Simplification of COVID-19 Texts (2022.lrec-1)
Copied to clipboard
| Challenge: | Specialist high-quality information is typically first available in English, and it is written in a language that may be difficult to understand by most readers. |
| Approach: | They propose to use a new language resource to simplify COVID-19 texts . they propose to employ four annotators who simplified over 6,000 sentences . |
| Outcome: | The proposed dataset improves readability from the original texts to their simplified versions. |
Multi-Word Lexical Simplification (2020.coling-main)
Copied to clipboard
| Challenge: | In text simplification, individual words are replaced with their simpler equivalents, but single word substitutions do not cover the full complexity of techniques humans use to approach text simulating. |
| Approach: | They propose a task of multi-word lexical simplification in which a sentence is made easier to understand by replacing its fragment with a simpler alternative. |
| Outcome: | The proposed method is based on a purpose-trained neural language model and evaluates against human and resource-based baselines. |