Papers by Mark Lee
Deep Contextualized Word Representations (N18-1)
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Matthew E. Peters, Mark Neumann, Mohit Iyyer, Matt Gardner, Christopher Clark, Kenton Lee, Luke Zettlemoyer
| Challenge: | a new type of deep contextualized word representation is proposed for language understanding problems . word vectors are learned functions of the internal states of a deep bidirectional language model . |
| Approach: | They propose a new type of deep contextualized word representation that models complex features of word use and how they vary across linguistic contexts. |
| Outcome: | The proposed representations improve the state of the art across six challenging NLP problems. |
DAEA: Enhancing Entity Alignment in Real-World Knowledge Graphs Through Multi-Source Domain Adaptation (2025.coling-main)
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| Challenge: | Entity Alignment (EA) is a critical task in Knowledge Graph (KG) integration. |
| Approach: | They propose a novel approach that leverages the data characteristics of synthetic benchmarks to improve performance in real-world datasets. |
| Outcome: | The proposed approach outperforms state-of-the-art models on real-world datasets and achieves a 29.94% improvement in Hits@1 on DOREMUS and 5.64% improvement on AGROLD. |
Can vectors read minds better than experts? Comparing data augmentation strategies for the automated scoring of children’s mindreading ability (2021.acl-long)
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| Challenge: | In-domain experts are recruited to reannotate augmented samples and determine to what extent each strategy preserves the original rating. |
| Approach: | They implement 7 different data augmentation strategies for the task of automatic scoring of children’s ability to understand others’ thoughts, feelings, and desires. |
| Outcome: | The data augmentation strategies outperform task-agnostic augmentations and automatic augmentation systems perform worst on the MIND-CA corpus. |
What if you said that differently?: How Explanation Formats Affect Human Feedback Efficacy and User Perception (2024.naacl-long)
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| Challenge: | Question answering models can often be black boxes, as their reasoning process is mostly opaque. |
| Approach: | They analyze the effect of rationales generated by QA models on user feedback and how well they enable users to understand and trust model answers. |
| Outcome: | The proposed model can be used to improve model responses by removing feedback from end users and enhancing model outputs by using natural language feedback. |
“What is on your mind?” Automated Scoring of Mindreading in Childhood and Early Adolescence (2020.coling-main)
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Venelin Kovatchev, Phillip Smith, Mark Lee, Imogen Grumley Traynor, Irene Luque Aguilera, Rory Devine
| Challenge: | Existing studies show that children who excel at mindreading are more likely to be identified as popular by classmates and have reciprocated friendships. |
| Approach: | They propose to automate the scoring of mindreading ability in middle childhood and early adolescence using a new corpus of 11,311 question-answer pairs in English from 1,066 children aged from 7 to 14 . |
| Outcome: | The proposed scoring system is based on 11,311 question-answer pairs in English from 1,066 children aged from 7 to 14 . the results demonstrate the applicability of state-of-the-art NLP solutions to a new domain and task. |
Code-Mixed Probes Show How Pre-Trained Models Generalise on Code-Switched Text (2024.lrec-main)
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| Challenge: | Code-switching is a prevalent linguistic phenomenon in which multilingual individuals seamlessly alternate between languages. |
| Approach: | They propose to use pre-trained language models to generalise to code-switched text . they use a dataset of well-formed naturalistic code-witched texts and parallel translations into the source languages to examine their results. |
| Outcome: | The proposed model generalises to code-switched text, shedding light on their ability to generalise representations to CS corpora. |
Integrating Question Classification and Deep Learning for improved Answer Selection (C18-1)
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| Challenge: | Question Answering (QA) is the task of automatically generating answers to questions posed in natural language. |
| Approach: | They propose a system for Answer Selection that integrates fine-grained Question Classification with a Deep Learning model designed for Answer selection. |
| Outcome: | The proposed system outperforms the current state of the art in all variations except one . the proposed system improves QA by reducing the search space of potential answers . |
ExpertQA: Expert-Curated Questions and Attributed Answers (2024.naacl-long)
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| Challenge: | a recent study examined the attribution and factuality of language models in domains . experts from various fields are using large language models for information-seeking scenarios . |
| Approach: | They evaluate language models' attribution and factuality by bringing domain experts in the loop . they collect expert-curated questions from 484 participants across 32 fields of study . |
| Outcome: | The results show that language models can provide factually correct answers in high-stakes fields, but they can also be harmful to experts. |