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Title: Cross-lingual Alignment vs Joint Training: A Comparative Study and A Simple Unified Framework

Paper content:
Learning multilingual representations of text has proven a successful method for many cross-lingual transfer learning tasks.
There are two main paradigms for learning such representations: (1) alignment, which maps different independently trained monolingual representations into a shared space, and (2) joint training, which directly learns unified multilingual representations using monolingual and cross-lingual objectives jointly.
In this paper, we first conduct direct comparisons of representations learned using both of these methods across diverse cross-lingual tasks.
Our empirical results reveal a set of pros and cons for both methods, and show that the relative performance of alignment versus joint training is task-dependent.
Stemming from this analysis, we propose a simple and novel framework that combines these two previously mutually-exclusive approaches.
Extensive experiments on various tasks demonstrate that our proposed framework alleviates limitations of both approaches, and outperforms existing methods on the MUSE bilingual lexicon induction (BLI) benchmark.
We further show that our proposed framework can generalize to contextualized representations and achieves state-of-the-art results on the CoNLL cross-lingual NER benchmark.
Continuous word representations (Mikolov et al., 2013a; Pennington et al., 2014; Bojanowski et al., 2017) have become ubiquitous across a wide range of NLP tasks.
In particular, methods for crosslingual word embeddings (CLWE) have proven a powerful tool for cross-lingual transfer for downstream tasks, such as text classification (Klementiev et al., 2012a) , dependency parsing (Ahmad et al., 2019) , named entity recognition (NER) (Xie et al., 2018; Chen et al., 2019) , natural language inference , language modeling (Adams et al., 2017) , and machine translation (MT) (Zou et al., 2013; Artetxe et al., 2018b; .
The goal of these CLWE methods is to learn embeddings in a shared vector space for two or more languages.
There are two main paradigms for learning CLWE: cross-lingual alignment and joint training.
The most successful approach has been the cross-lingual embedding alignment method (Mikolov et al., 2013b) , which relies on the assumption that monolingually-trained continuous word embedding spaces share similar structure across different languages.
The underlying idea is to first independently train embeddings in different languages using monolingual corpora alone, and then learn a mapping to align them to a shared vector space.
Such a mapping can be trained in a supervised fashion using parallel resources such as bilingual lexicons (Xing et al., 2015; Smith et al., 2017; Joulin et al., 2018b; Jawanpuria et al., 2019) , or even in an unsupervised 2 manner based on distribution matching (Zhang et al., 2017a; Artetxe et al., 2018a; Zhou et al., 2019) .
Recently, it has been shown that alignment methods can also be effectively applied to contextualized word representations (Schuster et al., 2019; Aldarmaki & Diab, 2019) .
Another successful line of research for CLWE considers joint training methods, which optimize a monolingual objective predicting the context of a word in a monolingual corpus along with either a 1 Code will be released on publication.
2 In this paper, "supervision" refers to that provided by a parallel corpus or bilingual dictionaries.
hard or soft cross-lingual constraint.
Similar to alignment methods, some early works rely on bilingual dictionaries (Ammar et al., 2016; Duong et al., 2016) or parallel corpora (Luong et al., 2015; for direct supervision.
More recently, a seemingly naive unsupervised joint training approach has received growing attention due to its simplicity and effectiveness.
In particular, reports that simply training embeddings on concatenated monolingual corpora of two related languages using a shared vocabulary without any cross-lingual resources is able to produce higher accuracy than the more sophisticated alignment methods on unsupervised MT tasks.
Besides, for contextualized representations, unsupervised multilingual language model pretraining using a shared vocabulary has produced state-of-the-art results on multiple benchmarks 3 (Devlin et al., 2019; Artetxe & Schwenk, 2019; Lample & Conneau, 2019) .
Despite a large amount of research on both alignment and joint training, previous work has neither performed a systematic comparison between the two, analyzed their pros and cons, nor elucidated when we may prefer one method over the other.
Particularly, it's natural to ask: (1) Does the phenomenon reported in extend to other cross-lingual tasks?
(2) Can we employ alignment methods to further improve their proposed unsupervised joint training?
(3) If so, how would such a framework compare to supervised joint training methods that exploit equivalent resources?
(4) And lastly, can this framework generalize to contextualized representations?
In this work, we attempt to address these questions.
Specifically, we first evaluate and compare alignment versus joint training methods across three diverse tasks: BLI, cross-lingual NER, and unsupervised MT. We seek to characterize the conditions under which one approach outperforms the other, and glean insight on the reasons behind these differences.
Based on our analysis, we further propose a simple, novel, and highly generic framework that uses unsupervised joint training as initialization and alignment as refinement to combine both paradigms.
Our experiments demonstrate that our framework improves over both alignment and joint training baselines, and outperforms existing methods on the MUSE BLI benchmark.
Moreover, we show that our framework can generalize to contextualized representations, producing state-of-the-art results on the CoNLL cross-lingual NER benchmark.
To the best of our knowledge, this is the first framework that combines previously mutually-exclusive alignment and joint training methods.
In this paper, we systematically compare the alignment and joint training methods for CLWE.
We point out that the nature of each category of methods leads to certain strengths and limitations.
The empirical experiments on extensive benchmark datasets and various NLP tasks verified our analysis.
To further improve the state-of-art of CLWE, we propose a simple hybrid framework which combines the strength from both worlds and achieves significantly better performance in the BLI, MT and NER tasks.
Our work opens a promising new direction that combines two previously exclusive lines of research.
For future work, an interesting direction is to find a more optimal word sharing strategy.

assistant: text: This paper compares approaches to bilingual lexicon induction and shows which method performs better on lexicon, induction, and NER and MT tasks.