David Stap


2023

pdf
Improving Domain Robustness in Neural Machine Translation with Fused Topic Knowledge Embeddings
Danai Xezonaki | Talaat Khalil | David Stap | Brandon Denis
Proceedings of Machine Translation Summit XIX, Vol. 1: Research Track

Domain robustness is a key challenge for Neural Machine Translation (NMT). Translating text from a different distribution than the training set requires the NMT models to generalize well to unseen domains. In this work we propose a novel way to address domain robustness, by fusing external topic knowledge into the NMT architecture. We employ a pretrained denoising autoencoder and fuse topic information into the system during continued pretraining, and finetuning of the model on the downstream NMT task. Our results show that incorporating external topic knowledge, as well as additional pretraining can improve the out-of-domain performance of NMT models. The proposed methodology meets state-of-the-art on out-of-domain performance. Our analysis shows that a low overlap between the pretraining and finetuning corpora, as well as the quality of topic representations help the NMT systems become more robust under domain shift.

pdf
UvA-MT’s Participation in the WMT 2023 General Translation Shared Task
Di Wu | Shaomu Tan | David Stap | Ali Araabi | Christof Monz
Proceedings of the Eighth Conference on Machine Translation

This paper describes the UvA-MT’s submission to the WMT 2023 shared task on general machine translation. We participate in the constrained track in two directions: English Hebrew. In this competition, we show that by using one model to handle bidirectional tasks, as a minimal setting of Multilingual Machine Translation (MMT), it is possible to achieve comparable results with that of traditional bilingual translation for both directions. By including effective strategies, like back-translation, re-parameterized embedding table, and task-oriented fine-tuning, we obtained competitive final results in the automatic evaluation for both English Hebrew and Hebrew English directions.

pdf
ChatGPT is not a good indigenous translator
David Stap | Ali Araabi
Proceedings of the Workshop on Natural Language Processing for Indigenous Languages of the Americas (AmericasNLP)

This report investigates the continuous challenges of Machine Translation (MT) systems on indigenous and extremely low-resource language pairs. Despite the notable achievements of Large Language Models (LLMs) that excel in various tasks, their applicability to low-resource languages remains questionable. In this study, we leveraged the AmericasNLP competition to evaluate the translation performance of different systems for Spanish to 11 indigenous languages from South America. Our team, LTLAmsterdam, submitted a total of four systems including GPT-4, a bilingual model, fine-tuned M2M100, and a combination of fine-tuned M2M100 with $k$NN-MT. We found that even large language models like GPT-4 are not well-suited for extremely low-resource languages. Our results suggest that fine-tuning M2M100 models can offer significantly better performance for extremely low-resource translation.

pdf
Multilingual k-Nearest-Neighbor Machine Translation
David Stap | Christof Monz
Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing

k-nearest-neighbor machine translation has demonstrated remarkable improvements in machine translation quality by creating a datastore of cached examples. However, these improvements have been limited to high-resource language pairs, with large datastores, and remain a challenge for low-resource languages. In this paper, we address this issue by combining representations from multiple languages into a single datastore. Our results consistently demonstrate substantial improvements not only in low-resource translation quality (up to +3.6 BLEU), but also for high-resource translation quality (up to +0.5 BLEU). Our experiments show that it is possible to create multilingual datastores that are a quarter of the size, achieving a 5.3x speed improvement, by using linguistic similarities for datastore creation.

pdf
Viewing Knowledge Transfer in Multilingual Machine Translation Through a Representational Lens
David Stap | Vlad Niculae | Christof Monz
Findings of the Association for Computational Linguistics: EMNLP 2023

We argue that translation quality alone is not a sufficient metric for measuring knowledge transfer in multilingual neural machine translation. To support this claim, we introduce Representational Transfer Potential (RTP), which measures representational similarities between languages. We show that RTP can measure both positive and negative transfer (interference), and find that RTP is strongly correlated with changes in translation quality, indicating that transfer does occur. Furthermore, we investigate data and language characteristics that are relevant for transfer, and find that multi-parallel overlap is an important yet under-explored feature. Based on this, we develop a novel training scheme, which uses an auxiliary similarity loss that encourages representations to be more invariant across languages by taking advantage of multi-parallel data. We show that our method yields increased translation quality for low- and mid-resource languages across multiple data and model setups.

2022

pdf
Super-NaturalInstructions: Generalization via Declarative Instructions on 1600+ NLP Tasks
Yizhong Wang | Swaroop Mishra | Pegah Alipoormolabashi | Yeganeh Kordi | Amirreza Mirzaei | Atharva Naik | Arjun Ashok | Arut Selvan Dhanasekaran | Anjana Arunkumar | David Stap | Eshaan Pathak | Giannis Karamanolakis | Haizhi Lai | Ishan Purohit | Ishani Mondal | Jacob Anderson | Kirby Kuznia | Krima Doshi | Kuntal Kumar Pal | Maitreya Patel | Mehrad Moradshahi | Mihir Parmar | Mirali Purohit | Neeraj Varshney | Phani Rohitha Kaza | Pulkit Verma | Ravsehaj Singh Puri | Rushang Karia | Savan Doshi | Shailaja Keyur Sampat | Siddhartha Mishra | Sujan Reddy A | Sumanta Patro | Tanay Dixit | Xudong Shen
Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing

How well can NLP models generalize to a variety of unseen tasks when provided with task instructions? To address this question, we first introduce Super-NaturalInstructions, a benchmark of 1,616 diverse NLP tasks and their expert-written instructions. Our collection covers 76 distinct task types, including but not limited to classification, extraction, infilling, sequence tagging, text rewriting, and text composition. This large and diverse collection of tasks enables rigorous benchmarking of cross-task generalization under instructions—training models to follow instructions on a subset of tasks and evaluating them on the remaining unseen ones. Furthermore, we build Tk-Instruct, a transformer model trained to follow a variety of in-context instructions (plain language task definitions or k-shot examples). Our experiments show that Tk-Instruct outperforms existing instruction-following models such as InstructGPT by over 9% on our benchmark despite being an order of magnitude smaller. We further analyze generalization as a function of various scaling parameters, such as the number of observed tasks, the number of instances per task, and model sizes. We hope our dataset and model facilitate future progress towards more general-purpose NLP models.