Zobrazeno 1 - 10
of 801
pro vyhledávání: '"P Kanojia"'
This paper investigates data sampling strategies to create a benchmark for dialectal sentiment classification of Google Places reviews written in English. Based on location-based filtering, we collect a self-supervised dataset of reviews in Australia
Externí odkaz:
http://arxiv.org/abs/2410.11216
This paper investigates whether large language models (LLMs) are state-of-the-art quality estimators for machine translation of user-generated content (UGC) that contains emotional expressions, without the use of reference translations. To achieve th
Externí odkaz:
http://arxiv.org/abs/2410.06338
Autor:
Carmo, Félix do, Kanojia, Diptesh
The tutorial describes the concept of edit distances applied to research and commercial contexts. We use Translation Edit Rate (TER), Levenshtein, Damerau-Levenshtein, Longest Common Subsequence and $n$-gram distances to demonstrate the frailty of st
Externí odkaz:
http://arxiv.org/abs/2410.05881
Machine translation (MT) of user-generated content (UGC) poses unique challenges, including handling slang, emotion, and literary devices like irony and sarcasm. Evaluating the quality of these translations is challenging as current metrics do not fo
Externí odkaz:
http://arxiv.org/abs/2410.03277
Autor:
Qian, Shenbin, Sindhujan, Archchana, Kabra, Minnie, Kanojia, Diptesh, Orăsan, Constantin, Ranasinghe, Tharindu, Blain, Frédéric
Leveraging large language models (LLMs) for various natural language processing tasks has led to superlative claims about their performance. For the evaluation of machine translation (MT), existing research shows that LLMs are able to achieve results
Externí odkaz:
http://arxiv.org/abs/2410.03278
Despite excellent results on benchmarks over a small subset of languages, large language models struggle to process text from languages situated in `lower-resource' scenarios such as dialects/sociolects (national or social varieties of a language), C
Externí odkaz:
http://arxiv.org/abs/2409.12683
Novel view acoustic synthesis (NVAS) aims to render binaural audio at any target viewpoint, given a mono audio emitted by a sound source at a 3D scene. Existing methods have proposed NeRF-based implicit models to exploit visual cues as a condition fo
Externí odkaz:
http://arxiv.org/abs/2406.08920
Audio-Visual Segmentation (AVS) aims to identify, at the pixel level, the object in a visual scene that produces a given sound. Current AVS methods rely on costly fine-grained annotations of mask-audio pairs, making them impractical for scalability.
Externí odkaz:
http://arxiv.org/abs/2403.14203
Autor:
Delfani, Jaleh, Orasan, Constantin, Saadany, Hadeel, Temizoz, Ozlem, Taylor-Stilgoe, Eleanor, Kanojia, Diptesh, Braun, Sabine, Schouten, Barbara
This study explores the use of Google Translate (GT) for translating mental healthcare (MHealth) information and evaluates its accuracy, comprehensibility, and implications for multilingual healthcare communication through analysing GT output in the
Externí odkaz:
http://arxiv.org/abs/2402.04023
Autor:
Gala, Jay, Jayakumar, Thanmay, Husain, Jaavid Aktar, M, Aswanth Kumar, Khan, Mohammed Safi Ur Rahman, Kanojia, Diptesh, Puduppully, Ratish, Khapra, Mitesh M., Dabre, Raj, Murthy, Rudra, Kunchukuttan, Anoop
We announce the initial release of "Airavata," an instruction-tuned LLM for Hindi. Airavata was created by fine-tuning OpenHathi with diverse, instruction-tuning Hindi datasets to make it better suited for assistive tasks. Along with the model, we al
Externí odkaz:
http://arxiv.org/abs/2401.15006