[Paper Review] Neural Task Representations as Weak Supervision for Model Agnostic Cross-Lingual Transfer

Sunil Kumar Jauhar, Michael Gamon+1 more|arXiv (Cornell University)|Nov 2, 2018

Topic Modeling33 references4 citations

TL;DR

This paper proposes a model-agnostic framework for cross-lingual transfer using neural task representations as weak supervision, requiring only unlabeled parallel data and a loss function over task representations. It achieves state-of-the-art or competitive performance on sentiment classification in German, French, and Japanese—outperforming strong baselines and complex methods—while learning semantically meaningful cross-lingual representations without direct supervision.

ABSTRACT

Natural language processing is heavily Anglo-centric, while the demand for models that work in languages other than English is greater than ever. Yet, the task of transferring a model from one language to another can be expensive in terms of annotation costs, engineering time and effort. In this paper, we present a general framework for easily and effectively transferring neural models from English to other languages. The framework, which relies on task representations as a form of weak supervision, is model and task agnostic, meaning that many existing neural architectures can be ported to other languages with minimal effort. The only requirement is unlabeled parallel data, and a loss defined over task representations. We evaluate our framework by transferring an English sentiment classifier to three different languages. On a battery of tests, we show that our models outperform a number of strong baselines and rival state-of-the-art results, which rely on more complex approaches and significantly more resources and data. Additionally, we find that the framework proposed in this paper is able to capture semantically rich and meaningful representations across languages, despite the lack of direct supervision.

Motivation & Objective

• To address the high cost and scarcity of annotated data for non-English languages in NLP.
• To enable effective transfer of pre-trained English neural models to low-resource languages with minimal engineering and data.
• To explore whether task representations can serve as weak supervision for learning cross-lingual semantics without direct alignment.
• To develop a general-purpose, architecture- and task-agnostic transfer framework that relies only on parallel monolingual corpora.
• To evaluate the framework’s ability to learn semantically meaningful cross-lingual representations without explicit supervision.

Proposed method

• Leverages intermediate task representations (layer before prediction layer) as weak supervision in unlabeled parallel data.
• Defines a loss function over these task representations to align semantic content across languages.
• Uses representation projection to map English task representations to target language embeddings via a shared, language-agnostic neural architecture.
• Maintains language-specific embeddings while sharing higher-level network layers to preserve task-specific semantics.
• Trains the model end-to-end using a combination of labeled English data and unlabeled parallel data.
• Employs cosine similarity between embeddings to evaluate cross-lingual semantic transfer.

Experimental results

Research questions

• RQ1 Can task representations from a pre-trained English model serve as effective weak supervision for cross-lingual transfer?
• RQ2 To what extent can a model learn semantically meaningful cross-lingual representations without direct supervision or parallel annotations?
• RQ3 How does the proposed framework compare to strong baselines and state-of-the-art methods in low-resource cross-lingual transfer?
• RQ4 Does the framework remain effective when combined with machine translation systems?
• RQ5 How do model size and architecture affect performance in cross-lingual transfer under this setup?

Key findings

• The framework outperforms multiple strong baselines, including an MT-based system trained on the same parallel data, on sentiment classification in German, French, and Japanese.
• On one target language (German), the model achieves performance that rivals or exceeds previous state-of-the-art results, despite using a simpler method and fewer resources.
• The model learns semantically rich cross-lingual representations, as evidenced by positive/negative sentiment words in English being close to their sentiment-equivalent counterparts in French by embedding similarity.
• Larger models tend to underperform due to overfitting and increased noise in the weak supervision signal from task representations.
• The framework is effective even without a high-quality MT system or bilingual dictionary, relying only on a parallel corpus and a loss over task representations.
• The approach generalizes well across languages, with 92% and 90% accuracy on same-language retrieval tasks in German, French, and Japanese, respectively.