Papers by Hany Hassan
Multi-Source Cross-Lingual Model Transfer: Learning What to Share (P19-1)
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| Challenge: | Cross-lingual transfer learning (CLTL) is a viable method for building NLP models for a low-resource target language . however, many languages lack the labeled training data necessary for training deep neural nets for varying NLP tasks. |
| Approach: | They propose a cross-lingual transfer learning method that leverages annotated data from other languages to build NLP models for a target language. |
| Outcome: | The proposed model achieves significant performance gains over prior art over multiple text classification and sequence tagging tasks including a large-scale industry dataset. |
Universal Neural Machine Translation for Extremely Low Resource Languages (N18-1)
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| Challenge: | a novel multilingual approach to machine translation is proposed for low resource languages . the proposed approach can achieve 23 BLEU on Romanian-English WMT2016 using a tiny parallel corpus of 6k sentences compared to the 18 BLUE of strong baseline system . |
| Approach: | They propose a transfer-learning approach to share lexical and sentence representations across multiple source languages into one target language. |
| Outcome: | The proposed approach achieves 23 BLEU on Romanian-English WMT2016 using a tiny parallel corpus of 6k sentences compared to the 18 BLUE of strong baseline system which uses multi-lingual training and back-translation. |
Z-Code++: A Pre-trained Language Model Optimized for Abstractive Summarization (2023.acl-long)
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Pengcheng He, Baolin Peng, Song Wang, Yang Liu, Ruochen Xu, Hany Hassan, Yu Shi, Chenguang Zhu, Wayne Xiong, Michael Zeng, Jianfeng Gao, Xuedong Huang
| Challenge: | Z-Code++ is a pre-trained language model optimized for abstractive text summarization. |
| Approach: | They propose a pre-trained language model optimized for abstractive text summarization that uses a two-phase pre-training technique to improve model's performance. |
| Outcome: | The proposed model outperforms the competing models on low-resource summarization tasks in zero-shot and few-shot settings. |
From Research to Production and Back: Ludicrously Fast Neural Machine Translation (D19-56)
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Young Jin Kim, Marcin Junczys-Dowmunt, Hany Hassan, Alham Fikri Aji, Kenneth Heafield, Roman Grundkiewicz, Nikolay Bogoychev
| Challenge: | Using the dominating submissions to the previous edition of the shared task, we develop improved teacher-student training via multi-agent dual-learning and noisy backward-forward translation for Transformer-based student models. |
| Approach: | They propose to use multi-agent dual-learning and noisy backward-forward translation to improve teacher-student training for Transformer-based student models. |
| Outcome: | The proposed model outperforms submissions to the previous edition of the WNGT efficiency shared task by 4 BLEU points and 10 BLUE points respectively. |
Building Multilingual Machine Translation Systems That Serve Arbitrary XY Translations (2022.naacl-main)
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| Challenge: | Multilingual Neural Machine Translation (MNMT) systems are often limited to many-to-one directions and suffer from poor performance in one-to one directions. |
| Approach: | They propose to build multilingual machine translation systems that serve arbitrary X-Y directions while leveraging multilinguality with a two-stage training strategy of pretraining and finetuning. |
| Outcome: | The proposed system outperforms baseline bilingual models and pivot translation models in most directions without the need for architecture change or extra data collection. |
Multi-task Learning for Multilingual Neural Machine Translation (2020.emnlp-main)
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| Challenge: | Existing multilingual neural machine translation systems rely on bitext training data, which is limited and costly to collect. |
| Approach: | They propose a multi-task learning framework that trains the model with the translation task on bitext data and two denoising tasks on monolingual data. |
| Outcome: | The proposed framework outperforms pre-training models for both NMT and cross-lingual transfer learning NLU tasks. |
Improving Multilingual Translation by Representation and Gradient Regularization (2021.emnlp-main)
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| Challenge: | Multilingual Neural Machine Translation models often produce low quality translations, often failing to produce outputs in the right target language. |
| Approach: | They propose a joint approach to regularize NMT models at both representation-level and gradient-level to reduce off-target translation occurrences and improve zero-shot translation performance. |
| Outcome: | The proposed approach reduces off-target translation occurrences and improves zero-shot translation performance by +5.59 and +10.38 BLEU on WMT and OPUS datasets. |
Selecting, Planning, and Rewriting: A Modular Approach for Data-to-Document Generation and Translation (D19-56)
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| Challenge: | Existing systems for document-level generation and translation are too complex to capture the complexity of the problem. |
| Approach: | They propose to adapt a large scale system trained on WMT data to a document in a different language. |
| Outcome: | The proposed system generates a textual document from structured data or a document in a different language. |
Do GPTs Produce Less Literal Translations? (2023.acl-short)
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| Challenge: | Large Language Models (LLMs) are general-purpose language models capable of many natural language generation or understanding tasks. |
| Approach: | They investigate how LLMs differ qualitatively from standard Neural Machine Translation models by measuring literalness and monotonicity. |
| Outcome: | The proposed models achieve close to state-of-the-art translation performance under few-shot prompting . the results are backed up by human evaluations and a newer MT quality metrics . |
Discovering Representation Sprachbund For Multilingual Pre-Training (2021.findings-emnlp)
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| Challenge: | Existing models perform poorly on many languages and cross-lingual tasks due to typological differences and contradictions between some languages. |
| Approach: | They propose to pre-train multilingual pre-trained models to handle cross-lingual tasks in one model. |
| Outcome: | The proposed model improves performance on cross-lingual tasks compared to baselines on multiple languages . |