On NMT Search Errors and Model Errors: Cat Got Your Tongue? (1908.10090v1)

Abstract: We report on search errors and model errors in neural machine translation (NMT). We present an exact inference procedure for neural sequence models based on a combination of beam search and depth-first search. We use our exact search to find the global best model scores under a Transformer base model for the entire WMT15 English-German test set. Surprisingly, beam search fails to find these global best model scores in most cases, even with a very large beam size of 100. For more than 50% of the sentences, the model in fact assigns its global best score to the empty translation, revealing a massive failure of neural models in properly accounting for adequacy. We show by constraining search with a minimum translation length that at the root of the problem of empty translations lies an inherent bias towards shorter translations. We conclude that vanilla NMT in its current form requires just the right amount of beam search errors, which, from a modelling perspective, is a highly unsatisfactory conclusion indeed, as the model often prefers an empty translation.

Analysis of NMT Search Errors and Model Errors

The paper "On NMT Search Errors and Model Errors: Cat Got Your Tongue?" explores the intricate issues surrounding search errors and model errors in neural machine translation (NMT). Authored by Felix Stahlberg and Bill Byrne, this work provides a rigorous examination of how exact inference methods can expose deficiencies in current NMT frameworks, specifically the Transformer architecture, regarding translation adequacy.

Exact Inference and Its Findings

The authors introduce an exact inference procedure employing both beam search and depth-first search (DFS) techniques to evaluate model score accuracy comprehensively. Applying this to the entire WMT15 English-German test set, it revealed that traditional beam search processes consistently fail to locate global best model scores, even with significantly large beam sizes. Notably, for more than half of the sentences analyzed, the NMT assigns its highest model score to an empty translation, thereby indicating a profound issue with the model's approach to adequacy. This exposes an inherent bias within NMT architectures towards shorter translations, as evidenced by the model's preference for empty hypotheses when exact search methodologies are utilized.

Implications on NMT and Adequacy

The empirical findings presented suggest that NMT models effectively require search errors to mitigate their flawed adequacy predictions. This paradoxical situation highlights model shortcomings and calls into question the reliability of vanilla NMT and its ability to generate meaningful translations. The preference for empty translations underlines a critical need for improved modeling techniques, particularly those addressing the apparent length bias in NMT systems. While length normalization methods are typically employed to correct these biases, they provide heuristic solutions without addressing the fundamental issues from a probabilistic standpoint.

Examination of Search and Model Errors

Through an analytical approach, the paper quantifies the extent of search and model errors within unconstrained NMT systems. Using established NMT architectures such as LSTM and the Transformer model, search errors manifest significantly regardless of system optimizations, with translation efficacy plummeting notably whenever search errors are minimized. This challenges the current assumptions around beam search's effectiveness and implicates potential deficiencies rooted within NMT's inherent design.

Future Directions and Research

This paper compels a reassessment of traditional NMT frameworks and encourages further research into developing models that accurately represent translation adequacy without reliance on search errors. Addressing these systemic inadequacies may involve considering probabilistic improvements, optimizing training objectives to reflect true adequacy standards, and employing calibrated probability estimates throughout the translation process. Such advancements could significantly enhance the reliability and robustness of NMT outputs, steering clear from rudimentary heuristic methods towards more theoretically sound solutions.

In conclusion, Stahlberg and Byrne's work provides a pivotal reflection on NMT inadequacies, offering a crucial lens through which future attempts to refine these models should be shaped. They underscore the importance of enhancing model capabilities directly within the NMT systems, rather than depending on external corrective measures. This paper serves as an impetus for advancing technologies in machine translation and reinforcing the significance of accurate translation adequacy in the AI domain.