97 citations · 160 across the 5 of their papers we have counts for
6 papers · 1 filter
Improving Multilingual Instruction Finetuning via Linguistically Natural and Diverse Datasets
Sathish Reddy Indurthi, Wenxuan Zhou, Shamil Chollampatt +4
Advancements in Large Language Models (LLMs) have significantly enhanced instruction-following capabilities. However, most Instruction Fine-Tuning (IFT) datasets are predominantly…
Can Automatic Post-Editing Improve NMT?
Shamil Chollampatt, Raymond Hendy Susanto, Liling Tan +1
Automatic post-editing (APE) aims to improve machine translations, thereby reducing human post-editing effort. APE has had notable success when used with statistical machine transl…
Lexically Constrained Neural Machine Translation with Levenshtein Transformer
Raymond Hendy Susanto, Shamil Chollampatt, Liling Tan
This paper proposes a simple and effective algorithm for incorporating lexical constraints in neural machine translation. Previous work either required re-training existing models…
A Multilayer Convolutional Encoder-Decoder Neural Network for Grammatical Error Correction
Shamil Chollampatt, Hwee Tou Ng
We improve automatic correction of grammatical, orthographic, and collocation errors in text using a multilayer convolutional encoder-decoder neural network. The network is initial…
Exploiting N-Best Hypotheses to Improve an SMT Approach to Grammatical Error Correction
Duc Tam Hoang, Shamil Chollampatt, Hwee Tou Ng
Grammatical error correction (GEC) is the task of detecting and correcting grammatical errors in texts written by second language learners. The statistical machine translation (SMT…
Neural Network Translation Models for Grammatical Error Correction
Shamil Chollampatt, Kaveh Taghipour, Hwee Tou Ng
Phrase-based statistical machine translation (SMT) systems have previously been used for the task of grammatical error correction (GEC) to achieve state-of-the-art accuracy. The su…