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Understanding and Analyzing Model Robustness and Knowledge-Transfer in Multilingual Neural Machine Translation using TX-Ray

Vageesh Saxena, Sharid Loáiciga, Nils Rethmeier

TL;DR

The thesis investigates robustness and knowledge-transfer in multilingual NMT under extremely low-resource conditions, employing TX-Ray to interpret cross-language transfer. It compares sequential transfer and joint multi-task learning, finding that sequential transfer generally yields stronger multilingual performance on a 40k-sentence corpus, while selective pruning tends to degrade performance and increase catastrophic forgetting. The work demonstrates that language-root proximity influences transfer effectiveness and shows how TX-Ray-derived neuron-knowledge distributions can visualize and quantify transfer dynamics. Practically, the findings highlight both the promise and limits of knowledge transfer and pruning in low-resource MNMT, and offer a framework for interpretable analysis of multi-language transfer phenomena.

Abstract

Neural networks have demonstrated significant advancements in Neural Machine Translation (NMT) compared to conventional phrase-based approaches. However, Multilingual Neural Machine Translation (MNMT) in extremely low-resource settings remains underexplored. This research investigates how knowledge transfer across languages can enhance MNMT in such scenarios. Using the Tatoeba translation challenge dataset from Helsinki NLP, we perform English-German, English-French, and English-Spanish translations, leveraging minimal parallel data to establish cross-lingual mappings. Unlike conventional methods relying on extensive pre-training for specific language pairs, we pre-train our model on English-English translations, setting English as the source language for all tasks. The model is fine-tuned on target language pairs using joint multi-task and sequential transfer learning strategies. Our work addresses three key questions: (1) How can knowledge transfer across languages improve MNMT in extremely low-resource scenarios? (2) How does pruning neuron knowledge affect model generalization, robustness, and catastrophic forgetting? (3) How can TX-Ray interpret and quantify knowledge transfer in trained models? Evaluation using BLEU-4 scores demonstrates that sequential transfer learning outperforms baselines on a 40k parallel sentence corpus, showcasing its efficacy. However, pruning neuron knowledge degrades performance, increases catastrophic forgetting, and fails to improve robustness or generalization. Our findings provide valuable insights into the potential and limitations of knowledge transfer and pruning in MNMT for extremely low-resource settings.

Understanding and Analyzing Model Robustness and Knowledge-Transfer in Multilingual Neural Machine Translation using TX-Ray

TL;DR

The thesis investigates robustness and knowledge-transfer in multilingual NMT under extremely low-resource conditions, employing TX-Ray to interpret cross-language transfer. It compares sequential transfer and joint multi-task learning, finding that sequential transfer generally yields stronger multilingual performance on a 40k-sentence corpus, while selective pruning tends to degrade performance and increase catastrophic forgetting. The work demonstrates that language-root proximity influences transfer effectiveness and shows how TX-Ray-derived neuron-knowledge distributions can visualize and quantify transfer dynamics. Practically, the findings highlight both the promise and limits of knowledge transfer and pruning in low-resource MNMT, and offer a framework for interpretable analysis of multi-language transfer phenomena.

Abstract

Neural networks have demonstrated significant advancements in Neural Machine Translation (NMT) compared to conventional phrase-based approaches. However, Multilingual Neural Machine Translation (MNMT) in extremely low-resource settings remains underexplored. This research investigates how knowledge transfer across languages can enhance MNMT in such scenarios. Using the Tatoeba translation challenge dataset from Helsinki NLP, we perform English-German, English-French, and English-Spanish translations, leveraging minimal parallel data to establish cross-lingual mappings. Unlike conventional methods relying on extensive pre-training for specific language pairs, we pre-train our model on English-English translations, setting English as the source language for all tasks. The model is fine-tuned on target language pairs using joint multi-task and sequential transfer learning strategies. Our work addresses three key questions: (1) How can knowledge transfer across languages improve MNMT in extremely low-resource scenarios? (2) How does pruning neuron knowledge affect model generalization, robustness, and catastrophic forgetting? (3) How can TX-Ray interpret and quantify knowledge transfer in trained models? Evaluation using BLEU-4 scores demonstrates that sequential transfer learning outperforms baselines on a 40k parallel sentence corpus, showcasing its efficacy. However, pruning neuron knowledge degrades performance, increases catastrophic forgetting, and fails to improve robustness or generalization. Our findings provide valuable insights into the potential and limitations of knowledge transfer and pruning in MNMT for extremely low-resource settings.

Paper Structure

This paper contains 72 sections, 15 equations, 59 figures, 23 tables.

Table of Contents

  1. Introduction
  2. Motivation
  3. Research Questions
  4. Related Research
  5. Deep Learning and Neural Machine Translation
  6. Multi-task and Continual Learning
  7. TX-Ray : Transfer eXplainability as pReference of Activations analYsis
  8. Catastrophic forgetting in Continual Learning
  9. BLEU-4 as the Evaluation Metric
  10. Network Architectures
  11. Sequence-to-Sequence learning
  12. Long Short-Term Memory (LSTM)
  13. LSTM Encoder
  14. LSTM Decoder
  15. Gated-Recurrent Units (GRU)
  16. ...and 57 more sections

Figures (59)

  • Figure 1: Multilingual Neural Machine Translation; Source language, English is translated to multiple Target languages like German, French, and Spanish.
  • Figure 2: Difference between Sequential-transfer learning and multi-task learning [ruder2019neural].
  • Figure 3: Activation Atlas: Grid of feature visualizations(one for each patch) showing how the network sees and understand different parts of the input image. carter2019activation
  • Figure 4: TX-Ray: Neuron: 877, demonstrating transfer of knowledge from the zero-shot(blue) task to the supervised(green) task. rethmeier2019txray
  • Figure 5: The displayed figure represents an LSTM-Encoder for Seq2Seq based Neural Machine Translation system. The source sentence is passed along with the initial hidden and cell states through the left side of the network, to extract the input features into the context vector.
  • ...and 54 more figures