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Found 7 papers, 0 papers with code
An Experiment on Speech-to-Text Translation Systems for Manipuri to English on Low Resource Setting
Based on this dataset, a benchmark evaluation is reported for the Manipuri-English Speech-to-Text translation using two approaches: 1) a pipeline model consisting of ASR (Automatic Speech Recognition) and Machine translation, and 2) an end-to-end Speech-to-Text translation.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+4
On the Transferability of Massively Multilingual Pretrained Models in the Pretext of the Indo-Aryan and Tibeto-Burman Languages
In recent times, machine translation models can learn to perform implicit bridging between language pairs never seen explicitly during training and showing that transfer learning helps for languages with constrained resources.
An Efficient Keyframes Selection Based Framework for Video Captioning
Describing a video is a challenging yet attractive task since it falls into the intersection of computer vision and natural language generation.
Ranked #1 on
Video Captioning
on Hindi MSR-VTT
The NITS-CNLP System for the Unsupervised MT Task at WMT 2020
We describe NITS-CNLP’s submission to WMT 2020 unsupervised machine translation shared task for German language (de) to Upper Sorbian (hsb) in a constrained setting i. e, using only the data provided by the organizers.
Multiple Captions Embellished Multilingual Multi-Modal Neural Machine Translation
In this work, we utilise the multiple captions from the Multi-30K dataset to increase the lexical diversity aided with the cross-lingual transfer of information among the languages in a multilingual setup.
Unsupervised Neural Machine Translation for English and Manipuri
Availability of bitext dataset has been a key challenge in the conventional machine translation system which requires surplus amount of parallel data.
Exploring the Efficacy of Large Language Models in Summarizing Mental Health Counseling Sessions: A Benchmark Study
Further, expert evaluation reveals that Mistral supersedes both MentalLlama and MentalBART based on six parameters -- affective attitude, burden, ethicality, coherence, opportunity costs, and perceived effectiveness.
