24 citations · 83 across the 67 of their papers we have counts for
6 papers · 2 filters
APE-then-QE: Correcting then Filtering Pseudo Parallel Corpora for MT Training Data Creation
Akshay Batheja, Sourabh Deoghare, Diptesh Kanojia +1
Automatic Post-Editing (APE) is the task of automatically identifying and correcting errors in the Machine Translation (MT) outputs. We propose a repair-filter-use methodology that…
SurreyAI 2023 Submission for the Quality Estimation Shared Task
Archchana Sindhujan, Diptesh Kanojia, Constantin Orasan +1
Quality Estimation (QE) systems are important in situations where it is necessary to assess the quality of translations, but there is no reference available. This paper describes t…
CreoleVal: Multilingual Multitask Benchmarks for Creoles
Heather Lent, Kushal Tatariya, Raj Dabre +18
Creoles represent an under-explored and marginalized group of languages, with few available resources for NLP research.While the genealogical ties between Creoles and a number of h…
Sarcasm in Sight and Sound: Benchmarking and Expansion to Improve Multimodal Sarcasm Detection
Swapnil Bhosale, Abhra Chaudhuri, Alex Lee Robert Williams +5
The introduction of the MUStARD dataset, and its emotion recognition extension MUStARD++, have identified sarcasm to be a multi-modal phenomenon -- expressed not only in natural la…
Evaluation of Chinese-English Machine Translation of Emotion-Loaded Microblog Texts: A Human Annotated Dataset for the Quality Assessment of Emotion Translation
Shenbin Qian, Constantin Orasan, Felix do Carmo +2
In this paper, we focus on how current Machine Translation (MT) tools perform on the translation of emotion-loaded texts by evaluating outputs from Google Translate according to a…
Applications and Challenges of Sentiment Analysis in Real-life Scenarios
Diptesh Kanojia, Aditya Joshi
Sentiment analysis has benefited from the availability of lexicons and benchmark datasets created over decades of research. However, its applications to the real world are a drivin…