Papers by Shamil Chollampatt
Cross-Sentence Grammatical Error Correction (P19-1)
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| Challenge: | Existing approaches to automatic grammatical error correction (GEC) ignore cross-sentence context . existing approaches only correct one sentence at a time and ignore useful contextual information . |
| Approach: | They propose to use an auxiliary encoder that encodes previous sentences and incorporates the encoding in the decoder via attention and gating mechanisms. |
| Outcome: | The proposed model improves over strong baselines on a synthetic dataset showing high performance in verb tense corrections that require cross-sentence context. |
CLAD-ST: Contrastive Learning with Adversarial Data for Robust Speech Translation (2023.emnlp-main)
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| Challenge: | Cascaded approach is the most popular choice for speech translation, but lacks robustness when dealing with noisy inputs. |
| Approach: | They propose a cascaded approach that uses an automatic speech recognition model and a machine translation model to translate speech in one language to text in another language. |
| Outcome: | The proposed approach achieves significant gains of up to 3 BLEU scores in English-German and English-French speech translation without hurting the translation quality on clean text. |
Lexically Constrained Neural Machine Translation with Levenshtein Transformer (2020.acl-main)
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| Challenge: | Existing approaches to incorporate lexical constraints in neural machine translation have been unsuccessful . |
| Approach: | They propose an algorithm that incorporates lexical constraints into neural machine translation. |
| Outcome: | The proposed method improves on English-German datasets without modification . it does not require any modification to the training procedure and can be easily applied at runtime with custom dictionaries. |
Neural Quality Estimation of Grammatical Error Correction (D18-1)
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| Challenge: | Grammatical error correction systems are expected to correct most learners’ writing errors, but in practice they often produce spurious corrections and fail to correct many errors, thereby misleading learners. |
| Approach: | They propose to use supervised learning to estimate the quality of GEC output sentences to help instructors decide whether to correct the errors or ignore them altogether. |
| Outcome: | The proposed model improves on a feature-based baseline and shows that the state-of-the-art system can be improved when quality scores are used as features for re-ranking the N-best candidates. |
A Reassessment of Reference-Based Grammatical Error Correction Metrics (C18-1)
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| Challenge: | Existing studies on the correlation of GEC metrics with human judgments were inconclusive . a recent study found that GLEU produces counter-intuitive scores in common test examples . |
| Approach: | They propose to use GLEU to evaluate grammatical error correction (GEC) systems . they also use statistical significance tests to assess their agreement with human judgments . |
| Outcome: | The proposed metrics show no significant advantage over MaxMatch (GLEU) the results contradict previous studies that claim GLEU superior . |
Cross-lingual Evaluation of Multilingual Text Generation (2025.coling-main)
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| Challenge: | Existing methods for multilingual text generation are limited by language and data leakage. |
| Approach: | They propose an annotation-free cross-lingual evaluation protocol for multilingual text generation . they first generate English references from the translated non-English inputs into English . |
| Outcome: | The proposed protocol shows a high correlation to the reference-based ROUGE metric in four languages on news text summarization. |
Improving Multilingual Instruction Finetuning via Linguistically Natural and Diverse Datasets (2024.findings-emnlp)
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Sathish Reddy Indurthi, Wenxuan Zhou, Shamil Chollampatt, Ravi Agrawal, Kaiqiang Song, Lingxiao Zhao, Chenguang Zhu
| Challenge: | Advancements in Large Language Models (LLMs) have significantly enhanced instruction-following capabilities, but most IFT datasets are predominantly in English, limiting model performance in other languages. |
| Approach: | They propose a method for collecting multilingual IFT datasets that preserves linguistic naturalness and ensures prompt diversity. |
| Outcome: | Experiments show that LLMs fine-tuned using this method show significant improvements in generative and discriminative tasks. |
Select, Prompt, Filter: Distilling Large Language Models for Summarizing Conversations (2023.emnlp-main)
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| Challenge: | Large language models (LLMs) can be expensive to train, deploy, and use for specific natural language generation tasks. |
| Approach: | They propose a method to distill ChatGPT and fine-tune smaller LMs for summarizing forum conversations using a semantic similarity metric. |
| Outcome: | The proposed method leads to significant improvements of up to 6.6 ROUGE-2 score by leveraging sufficient in-domain pseudo-labeled data over standard KD approach given the same size of training data. |
Can Automatic Post-Editing Improve NMT? (2020.emnlp-main)
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| Challenge: | APE has been successful with statistical machine translation systems but has not been as successful over neural machine translation (NMT) systems. |
| Approach: | They propose to train neural APE models on a corpus of human post-edits of NMT and compile a larger corpus to test their hypothesis. |
| Outcome: | The proposed model can improve a strong in-domain NMT system, challenging the current understanding in the field. |