Papers by James Allan
Conditional Natural Language Inference (2023.findings-emnlp)
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| Challenge: | False positive: widely-used feature-attribution explanation models are not suitable for finding conditions when sentences are long and are written independently. |
| Approach: | They propose a task of conditional natural language inference to explain sentence pairs that provide contradictory (different) information for different conditions. |
| Outcome: | The proposed model outperforms the full cross-encoder and other baselines in extracting conditions while maintaining comparable accuracy. |
Language Concept Erasure for Language-invariant Dense Retrieval (2024.emnlp-main)
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| Challenge: | Multilingual models aim for language-invariant representations but still encode language identity. |
| Approach: | They propose a multi-task learning framework that induces language invariance in multilingual retrieval by reducing language-specific signals in the embedding space. |
| Outcome: | The proposed learning framework improves language-invariant dense retrieval over baselines on English retrieval data and general multilingual corpora. |
A Multi-Task Architecture on Relevance-based Neural Query Translation (P19-1)
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| Challenge: | Existing models for cross-lingual information retrieval are not aware of the vocabulary distribution of the retrieval corpus. |
| Approach: | They propose a multi-task learning approach to train a Neural Machine Translation model with a Relevance-based Auxiliary Task (RAT) for search query translation. |
| Outcome: | The proposed model achieves 16% improvement over a strong baseline on Italian-English query-document dataset. |
Discovering Biases in Information Retrieval Models Using Relevance Thesaurus as Global Explanation (2024.emnlp-main)
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| Challenge: | Currently, local explanations are not effective in predicting the model’s behavior on unseen texts. |
| Approach: | They propose a method to build a relevance thesaurus containing semantically relevant query term and document term pairs which can augment BM25 scoring functions to better approximate the neural model’s predictions. |
| Outcome: | The proposed method can augment BM25 scoring functions to better approximate the neural relevance model’s predictions. |
AutoTriggER: Label-Efficient and Robust Named Entity Recognition with Auxiliary Trigger Extraction (2023.eacl-main)
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Dong-Ho Lee, Ravi Kiran Selvam, Sheikh Muhammad Sarwar, Bill Yuchen Lin, Fred Morstatter, Jay Pujara, Elizabeth Boschee, James Allan, Xiang Ren
| Challenge: | Named entity recognition models have shown impressive results in overcoming label scarcity and generalizing to unseen entities by leveraging distant supervision and auxiliary information such as explanations. |
| Approach: | They propose a framework that automatically generates and leverages “entity triggers” which are human-readable cues in the text that help guide the model to make better decisions. |
| Outcome: | The proposed framework outperforms the RoBERTa-CRF baseline by nearly 0.5 F1 points on three well-studied datasets. |
FEVER Breaker’s Run of Team NbAuzDrLqg (D19-66)
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| Challenge: | In the second workshop on Fact Extraction and VERification, the goal is to develop a fact-check system which can resolve "fake news" and misinformation problems. |
| Approach: | They propose to use a model to retrieve evidence when appropriate query terms could not be easily generated from the claim. |
| Outcome: | The proposed models were able to get both the evidence and label correct in 20% of the data. |