Search Results for author:
Found 23 papers, 18 papers with code
RomanLens: Latent Romanization and its role in Multilinguality in LLMs
For non-Latin script languages, we investigate the role of romanization - the representation of non-Latin scripts using Latin characters - as a bridge in multilingual processing.
An Empirical Comparison of Vocabulary Expansion and Initialization Approaches for Language Models
Language Models (LMs) excel in natural language processing tasks for English but show reduced performance in most other languages.
How Good is Zero-Shot MT Evaluation for Low Resource Indian Languages?
While machine translation evaluation has been studied primarily for high-resource languages, there has been a recent interest in evaluation for low-resource languages due to the increasing availability of data and models.
Airavata: Introducing Hindi Instruction-tuned LLM
We announce the initial release of "Airavata," an instruction-tuned LLM for Hindi.
RomanSetu: Efficiently unlocking multilingual capabilities of Large Language Models via Romanization
This study addresses the challenge of extending Large Language Models (LLMs) to non-English languages that use non-Roman scripts.
VerityMath: Advancing Mathematical Reasoning by Self-Verification Through Unit Consistency
Our findings indicate that our approach, which incorporates unit consistency, currently slightly underperforms compared to an approach that does not.
IndicTrans2: Towards High-Quality and Accessible Machine Translation Models for all 22 Scheduled Indian Languages
Prior to this work, there was (i) no parallel training data spanning all 22 languages, (ii) no robust benchmarks covering all these languages and containing content relevant to India, and (iii) no existing translation models which support all the 22 scheduled languages of India.
CTQScorer: Combining Multiple Features for In-context Example Selection for Machine Translation
We learn a regression model, CTQ Scorer (Contextual Translation Quality), that selects examples based on multiple features in order to maximize the translation quality.
Decomposed Prompting for Machine Translation Between Related Languages using Large Language Models
This study investigates machine translation between related languages i. e., languages within the same family that share linguistic characteristics such as word order and lexical similarity.
A Comprehensive Analysis of Adapter Efficiency
However, adapters have not been sufficiently analyzed to understand if PEFT translates to benefits in training/deployment efficiency and maintainability/extensibility.
Natural Language Understanding
parameter-efficient fine-tuning
Multi-Document Summarization with Centroid-Based Pretraining
In Multi-Document Summarization (MDS), the input can be modeled as a set of documents, and the output is its summary.
GEMv2: Multilingual NLG Benchmarking in a Single Line of Code
This problem is especially pertinent in natural language generation which requires ever-improving suites of datasets, metrics, and human evaluation to make definitive claims.
IndicNLG Benchmark: Multilingual Datasets for Diverse NLG Tasks in Indic Languages
Natural Language Generation (NLG) for non-English languages is hampered by the scarcity of datasets in these languages.
Data-to-text Generation with Variational Sequential Planning
We consider the task of data-to-text generation, which aims to create textual output from non-linguistic input.
IndicBART: A Pre-trained Model for Indic Natural Language Generation
We present IndicBART, a multilingual, sequence-to-sequence pre-trained model focusing on 11 Indic languages and English.
Data-to-text Generation with Macro Planning
Recent approaches to data-to-text generation have adopted the very successful encoder-decoder architecture or variants thereof.
Transition-Based Deep Input Linearization
Traditional methods for deep NLG adopt pipeline approaches comprising stages such as constructing syntactic input, predicting function words, linearizing the syntactic input and generating the surface forms.
Ranked #1 on
Data-to-Text Generation
on SR11Deep
University of Edinburgh's submission to the Document-level Generation and Translation Shared Task
The University of Edinburgh participated in all six tracks: NLG, MT, and MT+NLG with both English and German as targeted languages.
Data-to-text Generation with Entity Modeling
Recent approaches to data-to-text generation have shown great promise thanks to the use of large-scale datasets and the application of neural network architectures which are trained end-to-end.
Ranked #3 on
Data-to-Text Generation
on MLB Dataset
Data-to-Text Generation with Content Selection and Planning
Recent advances in data-to-text generation have led to the use of large-scale datasets and neural network models which are trained end-to-end, without explicitly modeling what to say and in what order.
