Papers by Artem Abzaliev
Acoustic Individual Identification of White-Faced Capuchin Monkeys Using Joint Multi-Species Embeddings (2025.acl-short)
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| Challenge: | acoustic identification of animals is an essential task for conservation and wildlife monitoring . but, many methods for automatic identification are hindered by lack of data . |
| Approach: | They explore cross-species pre-training to address the task of individual classification in white-faced capuchin monkeys. |
| Outcome: | The proposed methods can be used to identify calls from individual monkeys using acoustic embeddings from birds and humans. |
Towards Understanding the Relation between Gestures and Language (2022.coling-1)
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| Challenge: | a new study explores the relationship between gestures and language . we use contrastive learning to learn gesture embeddings . |
| Approach: | They adapt a semi-supervised multimodal model to learn gesture embeddings using Ted talks . they show gestures are predictive of the native language of the speaker . |
| Outcome: | The proposed model learns gesture embeddings from a multimodal dataset . it shows that gesture embeds are predictive of the native language of the speaker . |
Unsupervised Discrete Representations of American Sign Language (2024.emnlp-main)
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| Challenge: | Modern NLP models use discrete tokens to represent continuous signals, such as videos, audio, or gestures . modalities that are continuous are difficult to use with discrete models, such a LLM . |
| Approach: | They propose a method that discretizes sequences of fingerspelling signs into tokens . they also propose 'loss function' to improve interpretability of the tokens. |
| Outcome: | The proposed method improves the performance of the tokenizer on downstream tasks. |
Towards Dog Bark Decoding: Leveraging Human Speech Processing for Automated Bark Classification (2024.lrec-main)
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| Challenge: | Similar to humans, animals make extensive use of verbal and non-verbal forms of communication, including audio signals. |
| Approach: | They propose to use self-supervised speech representation models pre-trained on human speech to address dog bark classification tasks. |
| Outcome: | The proposed model improves dog recognition, breed identification, gender classification, and context grounding tasks. |
DELPHI: Data for Evaluating LLMs’ Performance in Handling Controversial Issues (2023.emnlp-industry)
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| Challenge: | a recent study of controversy-handling in large language models (LLMs) has shown that people may become increasingly dependent on such systems for information. |
| Approach: | They propose to construct a controversial questions dataset using a subset of a publicly available dataset. |
| Outcome: | The proposed dataset presents challenges concerning knowledge recency, safety, fairness, and bias. |
Has It All Been Solved? Open NLP Research Questions Not Solved by Large Language Models (2024.lrec-main)
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Oana Ignat, Zhijing Jin, Artem Abzaliev, Laura Biester, Santiago Castro, Naihao Deng, Xinyi Gao, Aylin Ece Gunal, Jacky He, Ashkan Kazemi, Muhammad Khalifa, Namho Koh, Andrew Lee, Siyang Liu, Do June Min, Shinka Mori, Joan C. Nwatu, Veronica Perez-Rosas, Siqi Shen, Zekun Wang, Winston Wu, Rada Mihalcea
| Challenge: | Recent advances in large language models have led to misleading public discourse that “it’s all been solved.” |
| Approach: | They identify 14 research areas encompassing 45 research directions that require new research and are not directly solvable by LLMs. |
| Outcome: | The research areas identified are 45 research directions that require new research and are not directly solvable by LLMs. |