Papers by Mayank Kulkarni
MergeME: Model Merging Techniques for Homogeneous and Heterogeneous MoEs (2025.naacl-long)
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Yuhang Zhou, Giannis Karamanolakis, Victor Soto, Anna Rumshisky, Mayank Kulkarni, Furong Huang, Wei Ai, Jianhua Lu
| Challenge: | State-of-the-art methods for merging expert models with different architectures do not address parameter interference and require extensive fine-tuning to restore performance. |
| Approach: | They propose a method for merging experts with different architectures into a unified Mixture-of-Experts model with a goal of enhancing performance in each domain while retaining effectiveness on general tasks. |
| Outcome: | Experiments across multiple domains show that the proposed methods reduce fine-tuning costs and improve performance over state-of-the-art methods. |
EntSUM: A Data Set for Entity-Centric Extractive Summarization (2022.acl-long)
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| Challenge: | Existing methods for controllable summarization fail to generate entity-centric summaries. |
| Approach: | They propose to use a human-annotated data set EntSUM to generate controllable summarization with a focus on named entities as the aspects to control. |
| Outcome: | The proposed data set shows that existing methods fail to generate entity-centric summaries. |
MASSIVE-Agents: A Benchmark for Multilingual Function-Calling in 52 Languages (2025.findings-emnlp)
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| Challenge: | Using the original dataset, we cleaned up the MASSIVE dataset and reformatted it for evaluation within the Berkeley Function-Calling Leaderboard framework. |
| Approach: | They present a new benchmark for assessing multilingual function calling across 52 languages . they clean the original MASSIVE dataset and reformat it for evaluation . |
| Outcome: | The new benchmark covers 55 functions and 286 arguments in 52 languages. |
Learning Rich Representation of Keyphrases from Text (2022.findings-naacl)
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| Challenge: | Prior work has referred to extractive (part of document) or abstractive (not part of document). |
| Approach: | They propose to use a new pre-training objective to introduce keyphrases into transformer language models in discriminative and generative settings. |
| Outcome: | The proposed model improves performance in discriminative and generative settings and also improves on named entity recognition, question answering, relation extraction and abstractive summarization tasks. |
Extractive Entity-Centric Summarization as Sentence Selection using Bi-Encoders (2022.aacl-short)
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| Challenge: | Entity-centric summarization is a type of controllable summarizing that aims to produce a summary specific to a given target entity. |
| Approach: | They propose to recast a sentence selection task as a controllable summarization using a dataset supported by EntSUM. |
| Outcome: | The proposed framework outperforms the current state-of-the-art in the sentence selection task and outperformed the competitive entity-centric Lead 3 heuristic by 1.1 F1. |
Towards a Unified Multi-Domain Multilingual Named Entity Recognition Model (2023.eacl-main)
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Mayank Kulkarni, Daniel Preotiuc-Pietro, Karthik Radhakrishnan, Genta Indra Winata, Shijie Wu, Lingjue Xie, Shaohua Yang
| Challenge: | Named Entity Recognition is a key task whose performance is sensitive to genre and language. |
| Approach: | They propose a setup for Named Entity Recognition which includes multi-domain and multilingual training and evaluation across 13 domains and 4 languages. |
| Outcome: | The proposed model improves on 13 domains and 4 languages across 13 domain and 4 language domains. |
Multi-Domain Named Entity Recognition with Genre-Aware and Agnostic Inference (2020.acl-main)
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| Challenge: | Named entity recognition (NER) is a key component of many text processing pipelines. |
| Approach: | They propose a new architecture tailored to the task of identifying named entities with data from multiple genres. |
| Outcome: | The proposed architecture outperforms baseline and competitive methods on all three setups with differences ranging between +1.95 to +3.11 average F1 across multiple genres when compared to standard approaches. |
Overcoming Catastrophic Forgetting in Massively Multilingual Continual Learning (2023.findings-acl)
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Genta Winata, Lingjue Xie, Karthik Radhakrishnan, Shijie Wu, Xisen Jin, Pengxiang Cheng, Mayank Kulkarni, Daniel Preotiuc-Pietro
| Challenge: | Existing methods to handle catastrophic forgetting fail to retain knowledge learnt in the past when sudden shifts occur in training data distributions. |
| Approach: | They propose a learning rate scheduling method that preserves new information without strongly overwriting past knowledge. |
| Outcome: | The proposed method preserves new information without overwriting past knowledge in a multilingual continuous learning framework. |
Clustering-based Sampling for Few-Shot Cross-Domain Keyphrase Extraction (2024.findings-eacl)
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| Challenge: | Scientific domain-specific pre-training has led to state-of-the-art keyphrase extraction performance with a majority of benchmarks being within the domain. |
| Approach: | They propose to leverage topic information available in the data to build a clustering-based sampling approach that facilitates selecting a few samples to label from the target domain . |
| Outcome: | The proposed approach leads to 26.35 points in performance when compared to selecting few-shot samples uniformly at random. |
An Empirical Analysis of Leveraging Knowledge for Low-Resource Task-Oriented Semantic Parsing (2023.findings-acl)
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Mayank Kulkarni, Aoxiao Zhong, Nicolas Guenon des mesnards, Sahar Movaghati, Mukund Sridhar, He Xie, Jianhua Lu
| Challenge: | Task-oriented semantic parsing is a new approach to represent the meaning of user requests with arbitrarily nested semantics. |
| Approach: | They propose to use knowledge-enhanced encoders to parse user requests with arbitrarily nested semantics. |
| Outcome: | The proposed model improves performance in low-resource and low-compute settings. |
Cross-lingual Few-Shot Learning on Unseen Languages (2022.aacl-main)
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| Challenge: | Large pre-trained language models have demonstrated the ability to obtain good performance on downstream tasks with limited examples in resource-rich languages. |
| Approach: | They propose to use a downstream sentiment analysis task to analyze the effectiveness of several few-shot learning strategies across 12 languages, including 8 unseen languages, to compare results. |
| Outcome: | The proposed model, XLM-R, gives the best performance on a task with few examples in resource-rich languages. |
EntSUMv2: Dataset, Models and Evaluation for More Abstractive Entity-Centric Summarization (2023.emnlp-main)
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| Challenge: | Entity-centric summarization is a form of controllable summarizing that aims to generate a summary for a specific entity given a document. |
| Approach: | They propose to use a more abstract version of the original entity-centric ENTSUM summarization dataset to generate a shorter annotated summary for downstream users. |
| Outcome: | The proposed method is more abstract and uses supervised fine-tuning and large-scale instruction tuning to provide more specific and useful summaries for downstream users. |