Papers by Sameen Maruf
Lifelong Explainer for Lifelong Learners (2021.emnlp-main)
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| Challenge: | Existing explanation methods are inefficient when explaining a static black-box model. |
| Approach: | They propose a Lifelong Explanation approach that continuously trains a student explainer under the supervision of a teacher on different tasks undertaken in LL. |
| Outcome: | The proposed approach can be extended to include a teacher and maintain the same level of faithfulness to the black-box model as the student explainer while being up to 102 times faster at test time. |
Document Context Neural Machine Translation with Memory Networks (P18-1)
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| Challenge: | Experimental results show that our model exploits both source and target document context. |
| Approach: | They propose a document-level neural machine translation model which takes both source and target document context into account using memory networks. |
| Outcome: | The proposed model outperforms previous work in terms of BLEU and METEOR in English translations. |
Selective Attention for Context-aware Neural Machine Translation (N19-1)
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| Challenge: | Recent work in context-aware NMT considers only a few previous sentences as context . current systems fail to achieve fluent, good quality translation for a full document . |
| Approach: | They propose a top-down approach to hierarchical attention for context-aware NMT which uses sparse attention to selectively focus on relevant sentences in the document context. |
| Outcome: | The proposed approach outperforms context-agnostic baselines and context-based baselines on English-German datasets. |
Learning to Explain: Generating Stable Explanations Fast (2021.acl-long)
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| Challenge: | Existing methods for explaining outcome of machine learning models produce explanations, or rationales, which identify the attributions of features in an input example. |
| Approach: | They propose a Learning to Explain approach that learns the behaviour of an underlying explanation algorithm simultaneously from all training examples. |
| Outcome: | The proposed approach is 5 to 7.5104 times faster than existing models and has comparable faithfulness to the black-box model. |
Monash University’s Submissions to the WNGT 2019 Document Translation Task (D19-56)
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| Challenge: | Despite the boom of work on document-level machine translation in the past two years, there has been a lack of the application of the proposed approaches to MT shared tasks. |
| Approach: | They propose to employ an established document-level neural machine translation model for the shared task of Rotowire document translation organised by the 3rd Workshop on Neural Generation and Translation (WNGT 2019). |
| Outcome: | The proposed model achieves a BLEU score of 39.83 for En-De and 45.06 for De-En translation directions on the Rotowire test set. |
Contextual Neural Machine Translation Improves Translation of Cataphoric Pronouns (2020.acl-main)
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| Challenge: | Recent studies have focused on past sentences as context with a focus on anaphora translation. |
| Approach: | They propose to use future context to improve NMT performance by comparing a contextual NMT model trained with past context to a context-agnostic model. |
| Outcome: | The proposed model outperforms the context-agnostic Transformer and shows comparable and in some cases improved performance. |