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Such contextual dynamic representations are obtained via deep neural models pretrained on large text collections through general objectives such as (masked) language modeling (Devlin et al. 2019; Liu et al. 2019b). Multilingual text encoders pretrained on 100+ languages, such as multilingual BERT (mBERT) (Devlin et al. 2019) or XLM(-R) (Conneau and Lample 2019; Conneau et al. 2020a), have become a de facto standard for multilingual representation learning and cross-lingual transfer in natural language processing (NLP). These models demonstrate state-of-the-art performance in a wide range of supervised language understanding and language generation tasks (Ponti et al. 2020; Liang et al. 2020): the general-purpose language knowledge obtained during pretraining is successfully specialized using task-specific training (i.e., fine-tuning). Multilingual transformers have been rendered especially effective in zero-shot transfer settings: a typical modus operandi is fine-tuning a pretrained multilingual encoder with task-specific data of a source language (typically English) and then using it directly in a target language. The effectiveness of cross-lingual transfer with multilingual transformers, however, has more recently been shown to highly depend on the typological proximity between languages as well as the size of the pretraining corpora in the target language (Hu et al. 2020; Lauscher et al. 2020; Zhao et al. 2021a).
In order to address all these questions, we present a systematic empirical study and profile the suitability of state-of-the-art pretrained multilingual encoders for different CLIR tasks and diverse language pairs, across unsupervised, supervised, and transfer setups. We evaluate state-of-the-art general-purpose pretrained multilingual encoders (mBERT Devlin et al. 2019 and XLM Conneau and Lample 2019) with a range of encoding variants, and also compare them to provenly robust CLIR approaches based on static CLWEs, as well as to specialized variants of multilingual encoders fine-tuned to encode sentence semantics (Artetxe et al. 2019; Feng et al. 2020; Reimers and Gurevych 2020, inter alia). Finally, we compare the unsupervised CLIR approaches based on these multilingual transformers with their counterparts fine-tuned on English relevance signal from different domains/collections. Our key contributions and findings are summarized as follows:
(1) We empirically validate (Sect. 4.2) that, without any task-specific fine-tuning, multilingual encoders such as mBERT and XLM fail to outperform CLIR approaches based on static CLWEs. Their performance also crucially depends on how one encodes semantic information with the models (e.g., treating them as sentence/document encoders directly versus averaging over constituent words and/or subwords).
(2) We show that multilingual sentence encoders, fine-tuned on labeled data from sentence pair tasks like natural language inference or semantic text similarity as well as using parallel sentences, substantially outperform general-purpose models (mBERT and XLM) in sentence-level CLIR (Sect. 4.3); further, they can be leveraged for localized relevance matching and in such a pooling setup improve the performance of unsupervised document-level CLIR (Sect. 4.4).
(3) Supervised neural rankers (also based on multilingual transformers like mBERT) trained on English relevance judgments from different collections (i.e., zero-shot language and domain transfer) do not surpass the best-performing unsupervised CLIR approach based on multilingual sentence encoders, either as standalone rankers or as re-rankers of the initial ranking produced by the unsupervised CLIR model based on multilingual sentence encoders (Sect. 5.1).
Multilingual Text Encoders based on the (masked) LM objectives have also been massively adopted in multilingual and cross-lingual NLP and IR applications. A multilingual extension of BERT (mBERT) is trained with a shared subword vocabulary on a single multilingual corpus obtained as concatenation of large monolingual data in 104 languages. The XLM model (Conneau and Lample 2019) extends this idea and proposes natively cross-lingual LM pretraining, combining causal language modeling (CLM) and translation language modeling (TLM).Footnote 2 Strong performance of these models in supervised settings is confirmed across a range of tasks on multilingual benchmarks such as XGLUE (Liang et al. 2020) and XTREME (Hu et al. 2020). However, recent work Reimers and Gurevych (2020) and Cao et al. (2020) has indicated that these general-purpose models do not yield strong results when used as out-of-the-box text encoders in an unsupervised transfer learning setup. We further investigate these preliminaries, and confirm this finding also for unsupervised ad-hoc CLIR tasks.
Specialized Multilingual Sentence Encoders An extensive body of work focuses on inducing multilingual encoders that capture sentence meaning. In Artetxe et al. (2019), the multilingual encoder of a sequence-to-sequence model is shared across languages and optimized to be language-agnostic, whereas Guo et al. (2018) rely on a dual Transformer-based encoder architecture instead (with tied/shared parameters) to represent parallel sentences. Rather than optimizing for translation performance directly, their approach minimizes the cosine distance between parallel sentences. A ranking softmax loss is used to classify the correct (i.e., aligned) sentence in the other language from negative samples (i.e., non-aligned sentences). In Yang et al. (2019a), this approach is extended by using a bidirectional dual encoder and adding an additive margin softmax function, which serves to push away non-translation-pairs in the shared embedding space. The dual-encoder approach is now widely adopted (Guo et al. 2018; Yang et al. 2020; Feng et al. 2020; Reimers and Gurevych 2020; Zhao et al. 2021b), and yields state-of-the-art multilingual sentence encoders which excel in sentence-level NLU tasks.
A related recent line of research targets cross-lingual transfer of (monolingual) rankers, where such rankers are typically trained on English data and then applied in a monolingual non-English setting (Shi et al. 2020, 2021; Zhang et al. 2021). This is different from our cross-lingual retrieval evaluation setting where queries and documents are in different languages. A systematic comparative study focused on the suitability of the multilingual text encoders for diverse ad-hoc CLIR tasks and language pairs is still lacking.
CLIR Evaluation and Application The cross-lingual ability of mBERT and XLM has been investigated by probing and analyzing their internals (Karthikeyan et al. 2020), as well as in terms of downstream performance (Pires et al. 2019; Wu and Dredze 2019). In CLIR, these models as well as dedicated multilingual sentence encoders have been evaluated on tasks such as cross-lingual question-answer retrieval (Yang et al. 2020), bitext mining (Ziemski et al. 2016; Zweigenbaum et al. 2018), and semantic textual similarity (STS) (Hoogeveen et al. 2015; Lei et al. 2016). Yet, the models have been primarily evaluated on sentence-level retrieval, while classic ad-hoc (unsupervised) document-level CLIR has not been in focus. Further, previous work has not provided a large-scale comparative study across diverse language pairs and with different model variants, nor has tried to understand and analyze the differences between sentence-level and document-level tasks, or the impact of domain versus language transfer. In this work, we aim to fill these gaps.
We first provide an overview of all pretrained multilingual models in our evaluation. We discuss general-purpose multilingual text encoders (Sect. 3.2), as well as specialized multilingual sentence encoders in Sect. 3.3. Finally, we describe the supervised rankers based on multilingual encoders (Sect. 3.4). For completeness, we first briefly describe the baseline CLIR model based on CLWEs (Sect. 3.1).
CLIR Models based on Multilingual Transformers. Left: Induce a static embedding space by encoding each vocabulary term in isolation; then refine the bilingual space for a specific language pair using the standard Procrustes projection. Middle: Aggregate different contextual representations of the same vocabulary term to induce static embedding space; then refine the bilingual space for a specific language pair using the standard Procrustes projection. Right: Direct encoding of a query-document pair with the multilingual encoder
Static Word Embeddings from Multilingual Transformers We first use multilingual transformers (mBERT and XLM) in two different ways to induce static word embedding spaces for all languages. In a simpler variant, we feed terms into the encoders in isolation (ISO), that is, without providing any surrounding context for the terms. This effectively constructs a static word embedding table similar to what is done in Sect. 3.1, and allows the CLIR model (Sect. 3.1) to operate at a non-contextual word level. An empirical CLIR comparison between ISO and CLIR operating on traditionally induced CLWEs (Litschko et al. 2019) then effectively quantifies how well multilingual encoders (mBERT and XLM) capture word-level representations (Vulić et al. 2020). 041b061a72