Papers by Richard James
A Framework for Flexible Extraction of Clinical Event Contextual Properties from Electronic Health Records (2025.acl-industry)
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| Challenge: | EHRs contain vast amounts of valuable clinical data, stored as unstructured text. |
| Approach: | They propose a method that uses existing NER+L methods to classify medical entities at scale using a named entity recognition and linking task. |
| Outcome: | The proposed model outperforms Bi-LSTM in minority class tasks with up to 28% of the time and 32% faster training time. |
Abstract Meaning Representation for Gesture (2022.lrec-1)
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| Challenge: | Abstract Meaning Representation (AMR) is an annotated graphbased representation that expresses the meaning of a sentence in terms of its predicate-argument structure. |
| Approach: | They propose an extension to Abstract Meaning Representation (AMR) that captures the meaning of gesture. |
| Outcome: | The proposed model is more challenging than standard AMR while integrating meaningful elements unique to gesture. |
Evaluating Unsupervised Argument Aligners via Generation of Conclusions of Structured Scientific Abstracts (2024.eacl-short)
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| Challenge: | Scientific abstracts provide a concise summary of research findings. |
| Approach: | They evaluate unsupervised approaches for extracting scientific arguments as aligned premise-conclusion pairs . they find mutual information outperforms other measures on this task . |
| Outcome: | The proposed methods outperform language models on the task of extracting scientific arguments from abstracts. |
Bi-Phone: Modeling Inter Language Phonetic Influences in Text (2023.acl-long)
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Abhirut Gupta, Ananya B. Sai, Richard Sproat, Yuri Vasilevski, James Ren, Ambarish Jash, Sukhdeep Sodhi, Aravindan Raghuveer
| Challenge: | Increasingly, people are forced to use the Web in languages they have low literacy in due to technology asymmetries. |
| Approach: | They propose a method to mine phoneme confusions for pairs of L1 and L2 and plug them into a generative model for synthetically producing corrupted L2 text. |
| Outcome: | The proposed method corrupts the popular language understanding benchmark SuperGLUE and improves performance. |
REPLUG: Retrieval-Augmented Black-Box Language Models (2024.naacl-long)
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Weijia Shi, Sewon Min, Michihiro Yasunaga, Minjoon Seo, Richard James, Mike Lewis, Luke Zettlemoyer, Wen-tau Yih
| Challenge: | Existing retrieval-augmented language models require access to internal representations to enhance performance. |
| Approach: | They introduce a retrieval-augmented language modeling framework that treats the language model as a black box and augments it with a tuneable retrieval model. |
| Outcome: | The proposed framework improves performance on language modeling tasks by 6.3% and 5.1%. |
Encoding Gesture in Multimodal Dialogue: Creating a Corpus of Multimodal AMR (2024.lrec-main)
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| Challenge: | Abstract Meaning Representation (AMR) was designed to represent sentence meaning in English text, but recent research has explored its adaptation to broader domains, including documents, dialogues, spatial information, cross-lingual tasks, and gesture. |
| Approach: | They propose to annotate a multimodal (speech and gesture) AMR corpus in a task-based setting and capture coreference relationships across modalities. |
| Outcome: | The proposed corpus captures coreference relationships across modalities, enabling fine-grained analysis of how gesture and natural language interact. |
Reimagining Retrieval Augmented Language Models for Answering Queries (2023.findings-acl)
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Wang-Chiew Tan, Yuliang Li, Pedro Rodriguez, Richard James, Xi Victoria Lin, Alon Halevy, Wen-tau Yih
| Challenge: | Large language models (LLMs) are expensive to train, deploy, and maintain, both financially and in terms of environmental impact. |
| Approach: | They present a reality check on large language models and compare their predictions to retrieval-augmented language models. |
| Outcome: | The proposed models fare better on question answering tasks and have become the foundation of impressive demos like Chat-GPT. |