337 citations · 699 across the 35 of their papers we have counts for
17 papers · 1 filter
Training with Ciphers to Enhance In-Context Terminology Learning For Translation from Rare Languages
Deevyanshu Malu, Deeptanshu Malu, Sunita Sarawagi
Fine-tuning LLMs with in-context examples (IC-Train) lets translation models adapt to new parallel data at deployment, but it is unclear whether they actually learn the translation…
Diverse In-Context Example Selection After Decomposing Programs and Aligned Utterances Improves Semantic Parsing
Mayank Kothyari, Sunita Sarawagi, Soumen Chakrabarti +2
LLMs are increasingly used as seq2seq translators from natural language utterances to structured programs, a process called semantic interpretation. Unlike atomic labels or token s…
Efficient Training of Language Models with Compact and Consistent Next Token Distributions
Ashutosh Sathe, Sunita Sarawagi
Maximizing the likelihood of the next token is an established, statistically sound objective for pre-training language models. In this paper we show that we can train better models…
CRUSH4SQL: Collective Retrieval Using Schema Hallucination For Text2SQL
Mayank Kothyari, Dhruva Dhingra, Sunita Sarawagi +1
Existing Text-to-SQL generators require the entire schema to be encoded with the user text. This is expensive or impractical for large databases with tens of thousands of columns.…
Benchmarking and Improving Text-to-SQL Generation under Ambiguity
Adithya Bhaskar, Tushar Tomar, Ashutosh Sathe +1
Research in Text-to-SQL conversion has been largely benchmarked against datasets where each text query corresponds to one correct SQL. However, natural language queries over real-l…
Improving RNN-Transducers with Acoustic LookAhead
Vinit S. Unni, Ashish Mittal, Preethi Jyothi +1
RNN-Transducers (RNN-Ts) have gained widespread acceptance as an end-to-end model for speech to text conversion because of their high accuracy and streaming capabilities. A typical…
Structured Case-based Reasoning for Inference-time Adaptation of Text-to-SQL parsers
Abhijeet Awasthi, Soumen Chakrabarti, Sunita Sarawagi
Inference-time adaptation methods for semantic parsing are useful for leveraging examples from newly-observed domains without repeated fine-tuning. Existing approaches typically bi…
Diverse Parallel Data Synthesis for Cross-Database Adaptation of Text-to-SQL Parsers
Abhijeet Awasthi, Ashutosh Sathe, Sunita Sarawagi
Text-to-SQL parsers typically struggle with databases unseen during the train time. Adapting parsers to new databases is a challenging problem due to the lack of natural language q…
Bootstrapping Multilingual Semantic Parsers using Large Language Models
Abhijeet Awasthi, Nitish Gupta, Bidisha Samanta +3
Despite cross-lingual generalization demonstrated by pre-trained multilingual models, the translate-train paradigm of transferring English datasets across multiple languages remain…
Accurate Online Posterior Alignments for Principled Lexically-Constrained Decoding
Soumya Chatterjee, Sunita Sarawagi, Preethi Jyothi
Online alignment in machine translation refers to the task of aligning a target word to a source word when the target sequence has only been partially decoded. Good online alignmen…
Overlap-based Vocabulary Generation Improves Cross-lingual Transfer Among Related Languages
Vaidehi Patil, Partha Talukdar, Sunita Sarawagi
Pre-trained multilingual language models such as mBERT and XLM-R have demonstrated great potential for zero-shot cross-lingual transfer to low web-resource languages (LRL). However…
Adaptive Discounting of Implicit Language Models in RNN-Transducers
Vinit Unni, Shreya Khare, Ashish Mittal +3
RNN-Transducer (RNN-T) models have become synonymous with streaming end-to-end ASR systems. While they perform competitively on a number of evaluation categories, rare words pose a…
Exploiting Language Relatedness for Low Web-Resource Language Model Adaptation: An Indic Languages Study
Yash Khemchandani, Sarvesh Mehtani, Vaidehi Patil +3
Recent research in multilingual language models (LM) has demonstrated their ability to effectively handle multiple languages in a single model. This holds promise for low web-resou…
What's in a Name? Are BERT Named Entity Representations just as Good for any other Name?
Sriram Balasubramanian, Naman Jain, Gaurav Jindal +2
We evaluate named entity representations of BERT-based NLP models by investigating their robustness to replacements from the same typed class in the input. We highlight that on sev…
Parallel Iterative Edit Models for Local Sequence Transduction
Abhijeet Awasthi, Sunita Sarawagi, Rasna Goyal +2
We present a Parallel Iterative Edit (PIE) model for the problem of local sequence transduction arising in tasks like Grammatical error correction (GEC). Recent approaches are base…
Topic Sensitive Attention on Generic Corpora Corrects Sense Bias in Pretrained Embeddings
Vihari Piratla, Sunita Sarawagi, Soumen Chakrabarti
Given a small corpus pertaining to a limited set of focused topics, our goal is to train embeddings that accurately capture the sense of words in the topic in spite…
Occurrence Statistics of Entities, Relations and Types on the Web
Aman Madaan, Sunita Sarawagi
The problem of collecting reliable estimates of occurrence of entities on the open web forms the premise for this report. The models learned for tagging entities cannot be expected…