33 citations · 68 across the 12 of their papers we have counts for
12 papers
There is No Theoretical Curse of Multilinguality For Embedding Space Structure
Niyati Bafna, Neha Verma, Vilém Zouhar +2
A central goal of multilingual NLP is to achieve high monolingual performance per language and cross-lingual alignment for large-scale language coverage with a multilingual model.…
ORBIT: Preserving Foundational Language Capabilities in GenRetrieval via Origin-Regulated Merging
Neha Verma, Nikhil Mehta, Shao-Chuan Wang +7
Despite the rapid advancements in large language model (LLM) development, fine-tuning them for specific tasks often results in the catastrophic forgetting of their general, languag…
DOTResize: Reducing LLM Width via Discrete Optimal Transport-based Neuron Merging
Neha Verma, Kenton Murray, Kevin Duh
Structured pruning methods designed for Large Language Models (LLMs) generally focus on identifying and removing the least important components to optimize model size. However, in…
Merging Feed-Forward Sublayers for Compressed Transformers
Neha Verma, Kenton Murray, Kevin Duh
With the rise and ubiquity of larger deep learning models, the need for high-quality compression techniques is growing in order to deploy these models widely. The sheer parameter c…
Merging Text Transformer Models from Different Initializations
Neha Verma, Maha Elbayad
Recent work on permutation-based model merging has shown impressive low- or zero-barrier mode connectivity between models from completely different initializations. However, this l…
Multilingual Pixel Representations for Translation and Effective Cross-lingual Transfer
Elizabeth Salesky, Neha Verma, Philipp Koehn +1
We introduce and demonstrate how to effectively train multilingual machine translation models with pixel representations. We experiment with two different data settings with a vari…
Exploring Representational Disparities Between Multilingual and Bilingual Translation Models
Neha Verma, Kenton Murray, Kevin Duh
Multilingual machine translation has proven immensely useful for both parameter efficiency and overall performance across many language pairs via complete multilingual parameter sh…
IsoVec: Controlling the Relative Isomorphism of Word Embedding Spaces
Kelly Marchisio, Neha Verma, Kevin Duh +1
The ability to extract high-quality translation dictionaries from monolingual word embedding spaces depends critically on the geometric similarity of the spaces -- their degree of…
Neural Natural Language Processing for Unstructured Data in Electronic Health Records: a Review
Irene Li, Jessica Pan, Jeremy Goldwasser +10
Electronic health records (EHRs), digital collections of patient healthcare events and observations, are ubiquitous in medicine and critical to healthcare delivery, operations, and…
FeTaQA: Free-form Table Question Answering
Linyong Nan, Chiachun Hsieh, Ziming Mao +14
Existing table question answering datasets contain abundant factual questions that primarily evaluate the query and schema comprehension capability of a system, but they fail to in…
DART: Open-Domain Structured Data Record to Text Generation
Linyong Nan, Dragomir Radev, Rui Zhang +21
We present DART, an open domain structured DAta Record to Text generation dataset with over 82k instances (DARTs). Data-to-Text annotations can be a costly process, especially when…
Improving Low-Resource Cross-lingual Document Retrieval by Reranking with Deep Bilingual Representations
Rui Zhang, Caitlin Westerfield, Sungrok Shim +5
In this paper, we propose to boost low-resource cross-lingual document retrieval performance with deep bilingual query-document representations. We match queries and documents in b…