Incorporating Structural Alignment Biases into an Attentional Neural Translation Model (1601.01085v1)

Abstract: Neural encoder-decoder models of machine translation have achieved impressive results, rivalling traditional translation models. However their modelling formulation is overly simplistic, and omits several key inductive biases built into traditional models. In this paper we extend the attentional neural translation model to include structural biases from word based alignment models, including positional bias, Markov conditioning, fertility and agreement over translation directions. We show improvements over a baseline attentional model and standard phrase-based model over several language pairs, evaluating on difficult languages in a low resource setting.

• The paper integrates structural biases such as positional, Markov, and fertility features to enhance attentional translation models.
• The paper introduces bilingual symmetry through joint training, achieving improved alignment and reduced perplexity across language pairs.
• The paper demonstrates that incorporating linguistic tendencies into neural models significantly boosts translation quality and robustness.

Overview of Structural Alignment Biases in Attentional Neural Translation Models

Machine translation has experienced significant advancements with the advent of neural encoder-decoder models, particularly attentional models. These models, characterized by an encoder that processes the source language and a decoder for the target language, have demonstrated commendable performance that rivals traditional statistical models. Despite their successes, neural translation models are acknowledged to be more simplistic, often lacking the structural biases inherent to traditional models.

The paper discussed here focuses on enhancing attentional neural translation models by integrating structural biases from word-based alignment models. The key biases that are incorporated include positional bias, Markov conditioning, fertility, and agreement in translation directions. These biases aim to leverage known linguistic tendencies to improve translation quality.

Key Concepts and Methodologies

1. Positional Bias: This refers to the tendency for words at similar relative positions in source and target languages to align. The paper proposes modifications to attentional models to account for positional similarity, thereby enhancing translation alignment fidelity.
2. Markov Conditioning: Traditional alignment models utilize Markov conditions to model local effects, like monotonic alignments. The paper incorporates these local dependencies into attentional models via feature-based approaches.
3. Fertility: This concept deals with the propensity for a word to translate into a consistent number of words. By extending the attentional model to include fertility biases, either through feature-based local fertility or through global fertility models, the authors hope to better capture phrase consistency in translations.
4. Bilingual Symmetry: Recognizing that alignments improve with joint inference between bidirectional models, the paper incorporates joint symmetry training into the attentional model. This is achieved through a pseudo-likelihood objective that regularizes alignment matrices from both translation directions.

Empirical Evaluations

The paper includes extensive empirical evaluations across several language pairs, including Russian, Estonian, Romanian, and Chinese to English translations. Results show consistent improvements in perplexity and BLEU scores when the proposed biases are integrated into attentional models. Specifically, the use of global fertility models in pre-trained settings demonstrated significant reduction in perplexity, and the application of bilingual symmetry achieved alignment improvements, particularly in difficult translation sentence structures.

Implications and Future Directions

The integration of structural biases into attentional models represents a valuable step toward more linguistically informed neural translation models. The results imply potential advancements in handling low-resource translation settings, wherein traditional statistical models often outperform simpler neural models. The inclusion of structural biases addresses the inadequacies of neural models and enhances their robustness.

Looking forward, the implications of this research extend to the theoretical understanding of how neural models can be better designed to capture intricate linguistic features inherent to human languages. Future research could explore additional linguistic elements such as syntactic and morphological features, thus further enriching the capability of neural translation systems. Additionally, application to larger datasets will confirm the scalability and effectiveness in real-world scenarios.

Overall, this paper provides a thoughtful contribution to the ongoing development of neural machine translation, offering pathways to improve translation accuracy and tackling current limitations with strategic incorporations of structural alignment biases.