Search Results for author:
Found 42 papers, 8 papers with code
INMT: Interactive Neural Machine Translation Prediction
In this paper, we demonstrate an Interactive Machine Translation interface, that assists human translators with on-the-fly hints and suggestions.
MEGA: Multilingual Evaluation of Generative AI
Most studies on generative LLMs have been restricted to English and it is unclear how capable these models are at understanding and generating text in other languages.
Grammatical Constraints on Intra-sentential Code-Switching: From Theories to Working Models
We make one of the first attempts to build working models for intra-sentential code-switching based on the Equivalence-Constraint (Poplack 1980) and Matrix-Language (Myers-Scotton 1993) theories.
Learnings from Technological Interventions in a Low Resource Language: Enhancing Information Access in Gondi
The primary obstacle to developing technologies for low-resource languages is the lack of representative, usable data.
Multilingual and code-switching ASR challenges for low resource Indian languages
For this purpose, we provide a total of ~600 hours of transcribed speech data, comprising train and test sets, in these languages including two code-switched language pairs, Hindi-English and Bengali-English.
Too Brittle To Touch: Comparing the Stability of Quantization and Distillation Towards Developing Lightweight Low-Resource MT Models
Leveraging shared learning through Massively Multilingual Models, state-of-the-art machine translation models are often able to adapt to the paucity of data for low-resource languages.
X-RiSAWOZ: High-Quality End-to-End Multilingual Dialogue Datasets and Few-shot Agents
We create a new multilingual benchmark, X-RiSAWOZ, by translating the Chinese RiSAWOZ to 4 languages: English, French, Hindi, Korean; and a code-mixed English-Hindi language.
The State and Fate of Linguistic Diversity and Inclusion in the NLP World
Language technologies contribute to promoting multilingualism and linguistic diversity around the world.
Language Modeling for Code-Mixing: The Role of Linguistic Theory based Synthetic Data
Training language models for Code-mixed (CM) language is known to be a difficult problem because of lack of data compounded by the increased confusability due to the presence of more than one language.
Automatic Speech Recognition (ASR)
Language Identification
+3
Estimating Code-Switching on Twitter with a Novel Generalized Word-Level Language Detection Technique
Word-level language detection is necessary for analyzing code-switched text, where multiple languages could be mixed within a sentence.
Phone Merging For Code-Switched Speech Recognition
Though the best performance gain of 1. 2{\%} WER was observed with manually merged phones, we show experimentally that the manual phone merge is not optimal.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
Accommodation of Conversational Code-Choice
We find that the saliency or markedness of a language in context directly affects the degree of accommodation observed.
Mining Hindi-English Transliteration Pairs from Online Hindi Lyrics
This paper describes a method to mine Hindi-English transliteration pairs from online Hindi song lyrics.
Functions of Code-Switching in Tweets: An Annotation Framework and Some Initial Experiments
Code-Switching (CS) between two languages is extremely common in communities with societal multilingualism where speakers switch between two or more languages when interacting with each other.
Unsung Challenges of Building and Deploying Language Technologies for Low Resource Language Communities
In this paper, we examine and analyze the challenges associated with developing and introducing language technologies to low-resource language communities.
Learnings from Technological Interventions in a Low Resource Language: A Case-Study on Gondi
The primary obstacle to developing technologies for low-resource languages is the lack of usable data.
Understanding Script-Mixing: A Case Study of Hindi-English Bilingual Twitter Users
Our analysis shows that both intra-sentential and inter-sentential script-mixing are present on Twitter and show different behavior in different contexts.
Crowdsourcing Speech Data for Low-Resource Languages from Low-Income Workers
Unfortunately, collecting labelled speech data in any language is an expensive and resource-intensive task.
Designing Language Technologies for Social Good: The Road not Taken
Development of speech and language technology for social good (LT4SG), especially those targeted at the welfare of marginalized communities and speakers of low-resource and under-served languages, has been a prominent theme of research within NLP, Speech, and the AI communities.
Predicting the Performance of Multilingual NLP Models
Recent advancements in NLP have given us models like mBERT and XLMR that can serve over 100 languages.
A Linguistic Annotation Framework to Study Interactions in Multilingual Healthcare Conversational Forums
In recent years, remote digital healthcare using online chats has gained momentum, especially in the Global South.
Global Readiness of Language Technology for Healthcare: What would it Take to Combat the Next Pandemic?
The COVID-19 pandemic has brought out both the best and worst of language technology (LT).
Annotated Speech Corpus for Low Resource Indian Languages: Awadhi, Bhojpuri, Braj and Magahi
In this paper we discuss an in-progress work on the development of a speech corpus for four low-resource Indo-Aryan languages -- Awadhi, Bhojpuri, Braj and Magahi using the field methods of linguistic data collection.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
Language Patterns and Behaviour of the Peer Supporters in Multilingual Healthcare Conversational Forums
In this work, we conduct a quantitative linguistic analysis of the language usage patterns of multilingual peer supporters in two health-focused WhatsApp groups in Kenya comprising of youth living with HIV.
Breaking Language Barriers with a LEAP: Learning Strategies for Polyglot LLMs
Our results show substantial advancements in multilingual understanding and generation across a diverse range of languages.
Are Large Language Model-based Evaluators the Solution to Scaling Up Multilingual Evaluation?
Large Language Models (LLMs) excel in various Natural Language Processing (NLP) tasks, yet their evaluation, particularly in languages beyond the top $20$, remains inadequate due to existing benchmarks and metrics limitations.
''Fifty Shades of Bias'': Normative Ratings of Gender Bias in GPT Generated English Text
Next, we systematically analyze the variation of themes of gender biases in the observed ranking and show that identity-attack is most closely related to gender bias.
MEGAVERSE: Benchmarking Large Language Models Across Languages, Modalities, Models and Tasks
We also perform a study on data contamination and find that several models are likely to be contaminated with multilingual evaluation benchmarks, necessitating approaches to detect and handle contamination while assessing the multilingual performance of LLMs.
MunTTS: A Text-to-Speech System for Mundari
We present MunTTS, an end-to-end text-to-speech (TTS) system specifically for Mundari, a low-resource Indian language of the Austo-Asiatic family.
DOSA: A Dataset of Social Artifacts from Different Indian Geographical Subcultures
Generative models are increasingly being used in various applications, such as text generation, commonsense reasoning, and question-answering.
METAL: Towards Multilingual Meta-Evaluation
This dataset is created specifically to evaluate LLM-based evaluators, which we refer to as meta-evaluation (METAL).
