The Devil Is in the Word Alignment Details: On Translation-Based Cross-Lingual Transfer for Token Classification Tasks
Authors: Benedikt Ebing, Goran Glavaš
Accepted at: acl25

The Devil Is in the Word Alignment Details: On Translation-Based Cross-Lingual Transfer for Token Classification Tasks

Benedikt Ebing and Goran Glavaš
University of Würzburg
Center for Artificial Intelligence and Data Science (CAIDAS)
{benedikt.ebing, goran.glavas}@uni-wuerzburg.de

Abstract

Translation-based strategies for cross-lingual transfer (XLT) such as translate-train—training on noisy target language data translated from the source language—and translate-test—evaluating on noisy source language data translated from the target language—are competitive XLT baselines. In XLT for token classification tasks, however, these strategies include label projection, the challenging step of mapping the labels from each token in the original sentence to its counterpart(s) in the translation. Although word aligners (WAs) are commonly used for label projection, the low-level design decisions for applying them to translation-based XLT have not been systematically investigated. Moreover, recent marker-based methods, which project labeled spans by inserting tags around them before (or after) translation, claim to outperform WAs in label projection for XLT. In this work, we revisit WAs for label projection, systematically investigating the effects of low-level design decisions on token-level XLT: (i) the algorithm for projecting labels between (multi-)token spans, (ii) filtering strategies to reduce the number of noisily mapped labels, and (iii) the pre-tokenization of the translated sentences. We find that all of these substantially impact translation-based XLT performance and show that, with optimized choices, XLT with WA offers performance at least comparable to that of marker-based methods. We then introduce a new projection strategy that ensembles translate-train and translate-test predictions and demonstrate that it substantially outperforms the marker-based projection. Crucially, we show that our proposed ensembling also reduces sensitivity to low-level WA design choices, resulting in more robust XLT for token classification tasks.

1 Introduction

In recent years, multilingual language models (mLMs) have de facto become the main vehicle of cross-lingual transfer (XLT): fine-tuned on labeled task data in a high-resource source language, mLMs can make predictions in target languages with few (few-shot XLT) to no (zero-shot XLT) labeled task instances (Wu and Dredze, 2019; Wang et al., 2019; Lauscher et al., 2020; Schmidt et al., 2022). While both encoder-only (Devlin et al., 2019; Conneau et al., 2020; He et al., 2023) and decoder-only (Team et al., 2024; Hui et al., 2024; Grattafiori et al., 2024) mLMs have demonstrated strong XLT performance for sequence classification tasks, in XLT for token classification tasks the comparatively smaller encoder-only mLMs, like XLM-R (Conneau et al., 2020), continue to outperform the much larger decoder mLMs (Ahuja et al., 2023; Le et al., 2024; Parekh et al., 2024).

Much of the above work highlights translation-based strategies as competitive approaches for XLT, where a machine translation (MT) model is used to either (1) generate noisy target language data by translating the original source language data before training, known as translate-train (T-Train), or (2) translate original target language instances into the (noisy) source language before inference, known as translate-test (T-Test) (Hu et al., 2020; Ruder et al., 2021; Ebrahimi et al., 2022; Aggarwal et al., 2022). More elaborate translation-based XLT strategies have recently been shown to further improve the transfer performance (Artetxe et al., 2023; Ebing and Glavaš, 2024).

The effectiveness of translation-based XLT, however, has predominantly been showcased on sequence-level classification tasks (Ruder et al., 2021; Oh et al., 2022; Artetxe et al., 2023). This is in part due to the fact that translation-based XLT for token classification tasks entails the (difficult) step of label projection. Traditionally, label projection is tackled with word aligners (WAs) (Och and Ney, 2003; Dyer et al., 2013; Dou and Neubig, 2021), which map each token in the source sequence to a corresponding token in the target se-
quence. Recent WA work leverages contextualized embeddings from mLMs (e.g., mBERT) to produce token alignments (Jalili Sabet et al., 2020; Dou and Neubig, 2021; Wang et al., 2022). Although WA research has a long-standing track record in NLP (Och and Ney, 2003; Dyer et al., 2013; Jalili Sabet et al., 2020; Dou and Neubig, 2021; Wang et al., 2022), standard WA evaluation protocols do not include translation-based XLT for token classification tasks. Because of this, the impact of low-level design decisions related to token-level XLT using WAs—such as (i) the exact algorithm for projecting the labels, (ii) filtering techniques to reduce the number of noisily mapped labels, and (iii) the process of inducing word boundaries (i.e. pre-tokenization) to the translated sentence before it can be aligned to the tokens in the original sentence—remain underinvestigated (Dou and Neubig, 2021; García-Ferrero et al., 2022).

In the meantime, marker-based label projection (Chen et al., 2023; Le et al., 2024) has largely replaced WA for label projection for token-level XLT. These approaches insert tags (e.g., "[", "]") around labeled spans of interest, either (i) before translation to preserve the markers throughout the translation process and recover the spans afterward (Chen et al., 2023), or (ii) post-translation by means of constrained decoding (Le et al., 2024).1

This line of work explicitly evaluates token-level translation-based XLT, demonstrating strong performance for both T-Train and T-Test. Furthermore, it renders WA-based XLT for token classification tasks inferior (Chen et al., 2023; Le et al., 2024). While these efforts provide the technical details for their proposed marker-based methods, they do not lay out the low-level design details for label projection with WAs. Therefore, we argue that they possibly underestimate translation-based XLT with WAs due to suboptimal design choices.

Contributions. Because of this, we (1) systematically investigate WA for token-level translation-based XLT. We start by evaluating the effect of low-level design decisions covering (i) the exact algorithm for mapping the labels between (multi-)token spans from the source sentence to the target sentence based on word alignments; (ii) filtering strategies to identify incomplete labeled span alignments, and (iii) the process of inducing word boundaries (i.e. pre-tokenization) to the translated sentence required to align the tokens to their counterparts in the original sentence. We demonstrate that these design choices can substantially impact translation-based XLT performance for token-level tasks. For example, using a language-specific pre-tokenizer instead of simple whitespace pre-tokenization improves the performance of T-Test by up to 12.6%. Overall, we find T-Test to be more sensitive to low-level design decisions of WA than T-Train. (2) We then extensively compare WA-based label projection—with the identified well-performing low-level design choices—against state-of-the-art marker-based label projection methods in token-level XLT on two established benchmarks encompassing 29 diverse languages. Contrary to prior claims (Chen et al., 2023; Le et al., 2024; Parekh et al., 2024), we find that optimized WA-based label projection matches or surpasses the performance of marker-based approaches in translation-based XLT on token-level tasks. (3) Moreover, we propose a more sophisticated method for token-level translation-based XLT with WAs based on ensembling T-Train and T-Test (Oh et al., 2022). For each token, we average the probability distributions over the labels produced by T-Train and T-Test. Our ensemble (ETT) improves the transfer performance substantially, outperforming state-of-the-art marker-based approaches. More importantly, ETT drastically reduces the sensitivity of T-Train and T-Test to low-level WA design decisions. (4) Finally, we show that our findings hold for different MT models (i.e., translation quality), WA models, and base LMs (i.e., encoder vs. decoder models). We publicly release our code and data: https://github.com/bebing93/devil-in-details.

2 Token-Level XLT via Word Alignment

We first detail our pipeline for label projection with WA focusing on the low-level design choices we investigate: mapping labeled spans, filtering strategies, and pre-tokenization (García-Ferrero et al., 2022). We then describe our new translation-based XLT approach for token-level tasks that ensembles T-Train and T-Test.

2.1 Label Projection with WA: Design Choices

Translation-based XLT entails two paradigms: (1) creating noisy target language training data by

1Further methods have been proposed that address label projection by constrained or contextualized generation of labeled spans given the translated input sentence (García-Ferrero et al., 2023; Parekh et al., 2024). We provide details on these methods in App. E and F. In our preliminary experiments these methods performed at most on par with Codec (see App. M).
Figure 1: Schematic overview of our pipeline for label projection using word aligners. From top to bottom, we display three cases—two for T-Train and one for T-Test: (i) a successful creation of a translated target language training instance \( \hat{T} \) from the original source language training instance \( S \), (ii) a discarded translated target language training instance \( \hat{T} \) due to incomplete alignments detected by our filtering strategies, and (iii) a partial label projection from a translated source language test instance \( \hat{S} \) to the original target language test instance \( T \) due to incomplete alignments detected by our filtering strategies. All cases exemplarily apply the Complete Source filter.

translating it from the original source language instances (T-Train) and (2) running inference on noisy source language instances translated from the original target language data (T-Test). For token-level tasks, label projection is required for both. We illustrate our pipeline for label projection with WA in Figure 1, comprising the following steps:

1. *Translating and Pre-Tokenizing* (\S2.1.3). We start from an original source language training instance \( S \) in T-Train (or an original target language test instance \( T \) in T-Test). Next, we translate the instance to the target language for T-Train (or source language for T-Test). We then pre-tokenize the translated sentence \( \hat{T} \) (or \( \hat{S} \)) for word alignment (i.e., we induce word boundaries).

2. *Word Alignment*. We produce the word alignments between \( S \) and \( \hat{T} \) for T-Train (or \( \hat{S} \) and \( T \) for T-Test). In the case of T-Test, we first gather the label predictions of the mLM on \( \hat{S} \).

3. *Filtering* (\S2.1.2). Based on the word alignment, we collect the labeled spans of tokens that need to be mapped from the source to the target sentence. Optionally, we apply our proposed filtering strategies to identify incomplete alignments between labeled spans of \( S \) and the corresponding tokens in \( \hat{T} \) for T-Train (\( \hat{S} \) and \( T \) for T-Test). For T-Train, if our filters are met for any labeled span, we discard the training instance \( \hat{T} \) (case two in Figure 1). For T-Test, we modify the procedure as we cannot discard test instances at inference time. Instead, for an incomplete labeled span, we do not map the corresponding labels from \( \hat{S} \) to \( T \) and assign the default label "O" (case three in Figure 1).

4. *Span Mapping* (\S2.1.1). Last, we apply our span mapping algorithm to project the labeled spans from \( S \) to \( \hat{T} \) for T-Train (or \( \hat{S} \) to \( T \) for T-Test).

2.1.1 *Span Mapping*

Translation-based XLT for token-level tasks requires mapping labeled spans of tokens from the source language to the target language sentence. Instead of simply projecting the token-level labels based on the word alignments produced in step 2 of our pipeline, we propose a more robust, span-based label projection, as illustrated in step 4 of Figure 1.

For T-Train, given a labeled span \( L \) in the source sentence \( S \) (e.g., \( L = \{s_1, \ldots, s_4\} \) in Figure 1) and the corresponding candidate labeled span \( \hat{L} \) in the translated sentence \( \hat{T} \) (e.g., \( \hat{L} = \{\hat{l}_1, \ldots, \hat{l}_4\} \) in Figure 1), we map the labels as follows. We assume a standard BIO scheme in which the first token in a span is labeled with a different tag (B-Tag) than the remaining span (I-Tag). We then map the label of the first token in \( L \) to the first token in \( \hat{L} \). Next, we map the label of the last token in \( L \) to all tokens in \( T \) between the first and the last token of \( \hat{L} \) (inclusively). The procedure ensures that the mapped labeled spans in \( \hat{T} \) are continuous and comply with the BIO scheme.

The span mapping for T-Test follows the same procedure. However, instead of mapping the gold labels, from the original source sentence \( S \) to the translated target sentence \( \hat{T} \), we now map the predictions of the mLM from the translated source sentence \( \hat{S} \) to the original target sentence \( T \).

We also experimented with the simple approach of naively projecting the labels directly based on the word alignments as produced in step 2. This simple projection strategy, however, consistently
yielded worse results in our initial experiments: we thus ran the full evaluation using only our span mapping strategy described above. We believe that the simple projection along word alignments performs poorly due to frequent changes in word order between languages.

2.1.2 Filtering Strategies
The success of the above span mapping directly depends on the quality of WA for a concrete language pair, which is affected by (i) the amount of parallel data for the pair used in WA training, (ii) the amount of monolingual data for the languages in question seen by the WA’s underlying mLM in pretraining (Dou and Neubig, 2021; Wang et al., 2022), and (iii) the linguistic proximity between the two languages (and in particular whether they have similar word order). To mitigate the impact of imperfect word alignment, we propose several strategies for detecting and filtering alignments of labeled spans with low quality.

Complete Source (COMP–SRC). We test if all tokens of a span \( L = \{s_m, ..., s_n\} \) in the source language sentence—\( S \) for T-Train and \( \hat{S} \) for T-Test—have an alignment, i.e., whether the corresponding tokens in \( S \) (or \( \hat{S} \)) are aligned to at least one token in \( \hat{T} \) (or \( T \)). Figure 1 illustrates an example of an incomplete source alignment: in the second and third cases, the token \( s_8 \) in \( S \) (or \( \hat{s}_8 \) in \( \hat{S} \)) is not aligned to any token in \( \hat{T} \) (or \( T \)). We assume that if \( L \) is partially unaligned, we miss information from the source language span. Hence, \( \hat{L} \) is more likely to be incomplete and thus incorrect.

Complete Target (COMP–TGT). The motivation for this filter is analogous to COMP–SRC: we select only the labeled span alignments for which all span tokens in the target language sentence—\( \hat{T} \) for T-Train and \( T \) for T-Test—are aligned to at least one token in the source language sentence. But since we do not have ground truth spans for the target language sentence, we apply the following proxy: we retain only the instances for which the tokens in \( \hat{L} \) form a continuous span. Case two and three in Figure 1 do not satisfy this filter, since \( \{t_6, t_7, t_9\} \) (or \( \{t_6, t_7, t_9\} \)) are discontinuous. We assume that if \( \hat{L} \) is partially unaligned, we risk adding additional information to the labeled span in the target language sentence that did not exist in the source language (e.g., by including a target language token distant from the rest of the span).

Complete Instance (COMP–INS). Following Chen et al. (2023), we verify that the number and type of labeled spans in \( S \) and \( \hat{T} \) match (e.g., if \( S \) has two spans with label LOC and one with label PER then \( \hat{T} \) must also have two LOC spans and one PER span). This filter can only be applied to T-Train as it needs access to the gold labels of \( S \).

Restricted Target (RSTR–TGT). This filter is specifically designed for T-Test. Preliminary experiments showed that single token labeled spans in \( \hat{S} \) are often aligned to multiple discontinuous target tokens in \( T \) that have a comparatively large number of unaligned tokens in between. On the one hand, applying our span mapping algorithm without filtering is too coarse-grained—such that it might map a single token labeled span from \( \hat{S} \) to a much longer sequence in \( T \)—on the other hand, applying COMP–TGT is too strict—such that it does not map the single token label span from \( \hat{S} \) at all. In summary, the former reduces precision while the latter degrades recall. RSTR–TGT aims at the trade-off between no filtering and COMP–TGT. It maps the single token label span from \( \hat{S} \) only to the first token in \( \hat{L} \) and assigns the default label "O" to all other tokens. This way, we still map the single token label spans from \( \hat{S} \) but limit their length to a single token in \( T \).

No Filtering (NO–FILT). We additionally compare our filtering strategies against a naive baseline: we omit the filtering step in our label projection pipeline and immediately apply our span mapping algorithm after we obtain the word alignments.

2.1.3 Pre-Tokenization
Word alignment for token-level tasks requires the original sentence and the translation to contain word boundaries (i.e., to be pre-tokenized). While the original sentence \( S \) (or \( T \)) is usually given in a pre-tokenized format, we still need to pre-tokenize the translation \( \hat{T} \) (or \( \hat{S} \)). We compare language-agnostic whitespace pre-tokenization (WS–TOK) against language-specific pre-tokenization (LS–TOK).\footnote{We provide the details on the tokenizers in App. G.} It is worth noting that it is more challenging to pre-tokenize the translated target language sentences \( \hat{T} \) in T-Train than the English translations \( \hat{S} \) in T-Test.

2.2 Ensembling T-Train and T-Test (ETT)
We next detail our proposed translation-based strategy for token-level XLT that ensembles the predictions of T-Train and T-Test (Oh et al., 2022). At inference time, the T-Train model produces
class logits for each token \( t_i \) in the target language sentence \( T \). In contrast, the T-Test model outputs class logits over each token \( s_j \) in the translated source language sentence \( \hat{S} \). We then run our WA-based pipeline for label projection to map the T-Test predictions from \( \hat{S} \) to \( T \). Instead of mapping the predicted class labels for each token, we now map the class logits. Finally, we average the class logits for each token in \( T \) obtained from T-Train and T-Test. If we cannot project the logits for a token in T-Test—because of a missing alignment or because a filter prevents the mapping of a labeled span—we only use the T-Train prediction. Importantly, our proposed ensembling is not restricted to a WA-based T-Train component and can also be combined with marker-based methods.

3 Experimental Setup

Machine Translation. For translation, we utilize the state-of-the-art massively multilingual NLLB model with 3.3B parameters (Team et al., 2022). Following prior work (Artetxe et al., 2023; Ebing and Glavaš, 2024), we decode using beam search with a beam size of 5.

Evaluation Tasks. We evaluate on two established token classification tasks: named entity recognition and slot labeling. Our experiments span 29 diverse languages, ranging from high-resource languages, represented well in the pretraining corpus of the base mLM, to low-resource languages, unseen by the mLM. We use English as our source language.3

Named Entity Recognition (NER). Our evaluation includes 18 of 20 languages from MasakhaNER 2.0 (Masakha) (Adelani et al., 2022) supported by the NLLB model used for translation. Masakha consists of underserved languages from Sub-Saharan Africa. As source data, we use the English training (14k instances) and validation portions (3250 instances) of CoNLL (Tjong Kim Sang and De Meulder, 2003). We add a softmax classifier on top of the mLM to predict the class for each token.

Slot Labeling (SL). We use the xSID dataset (van der Goot et al., 2021), which covers 11 diverse languages and dialects. xSID comprises only evaluation data, so we follow van der Goot et al. (2021) and use their publicly released English data for training and validation. The utterances are sourced from the Snips (Coucke et al., 2018) and Facebook (Schuster et al., 2019) SL datasets. After deduplication, we end up with over 36k instances for training and 300 for validation. As for NER, we add a softmax classifier on top of the mLM.

Label Projection. We compare our WA-based label projection approach against two state-of-the-art marker-based methods that tag the labeled spans and preserve the tags during translation.

Word Alignment (WA). In our main experiments, we resort to AccAlign (Wang et al., 2022), a WA that is based on the multilingual sentence encoder LaBSE (Feng et al., 2022).

EasyProject (Easy). We compare our WA-based approach against the marker-based label projection method of Chen et al. (2023). Before translation, Easy inserts tags ("[", "]") around labeled spans. The MT model is expected to preserve the tags during translation, enabling the reconstruction of the labels afterward. Note that Easy can only be used in T-Train and not in T-Test. Let \( T \) be the target language sentence at inference time; in T-Test, the model will make predictions on its English translation \( \hat{S} \); Easy would then insert markers into \( \hat{S} \) and back-translate to the target language, obtaining \( \hat{T} \); but \( \hat{T} \) will generally differ from \( T \), which is the actual sentence we need to label.

Codec. Our experiments further include Codec (Le et al., 2024), a label projection method that leverages constrained decoding as part of a two-step translation procedure. In the first step, the source sentence is simply translated into the target language (e.g., from English: “This is New York” to German: “Das ist New York”). Then, in the second step, tags are inserted around the labeled spans in the source sentence (English: “This is [ New York ]”). The marked sentence is fed again as input to the MT model: during decoding, the MT model is now constrained to generate only the tokens from the translation obtained in the first step (“Das”, “ist”, “New”, “York”) or a tag (“[”, “]”).

Downstream Fine-Tuning. We use XLM-R Large (Conneau et al., 2020) as our base mLM. For T-Test, we also experiment with DeBERTaV3 Large (He et al., 2023) and encoder-turned-decoder Llama 3 8B (LLM2Vec) (BehnamGhader et al., 2024) as English-centric models. In T-Train, we fine-tune on both the original English data and translated target language data, following Ebing and Glavaš (2024) who show that this is better than training only on translations. In T-Test, we train the models only on the original English data. We

3We provide a complete list of target languages in App. G.
<table>
  <tr>
    <th></th>
    <th>Masakha</th>
    <th>xSID</th>
    <th>Avg</th>
  </tr>
  <tr>
    <th colspan="4">Translate-Train</th>
  </tr>
  <tr>
    <td>NO-FILT</td>
    <td>65.5±1.2</td>
    <td>82.8±0.6</td>
    <td>74.2±1.0</td>
  </tr>
  <tr>
    <td>COMP-INS</td>
    <td>65.8±1.3</td>
    <td>82.8±0.5</td>
    <td>74.3±1.0</td>
  </tr>
  <tr>
    <td>+ COMP-TGT</td>
    <td>66.0±1.7</td>
    <td>82.0±0.7</td>
    <td>74.0±1.3</td>
  </tr>
  <tr>
    <td>+ COMP-TGT + COMP-SRC</td>
    <td>66.6±1.1</td>
    <td>82.0±0.9</td>
    <td>74.3±1.0</td>
  </tr>
  <tr>
    <th colspan="4">Translate-Test</th>
  </tr>
  <tr>
    <td>NO-FILT</td>
    <td>46.3±0.4</td>
    <td>68.2±0.4</td>
    <td>57.2±0.4</td>
  </tr>
  <tr>
    <td>RSTR-TGT</td>
    <td>57.9±0.5</td>
    <td>74.8±0.4</td>
    <td>66.4±0.5</td>
  </tr>
  <tr>
    <td>+ COMP-TGT</td>
    <td>57.8±0.5</td>
    <td>74.3±0.4</td>
    <td>66.0±0.5</td>
  </tr>
  <tr>
    <td>+ COMP-SRC</td>
    <td>58.6±0.5</td>
    <td>73.9±0.5</td>
    <td>66.2±0.5</td>
  </tr>
  <tr>
    <td>+ COMP-TGT + COMP-SRC</td>
    <td>58.0±0.5</td>
    <td>73.2±0.4</td>
    <td>65.6±0.5</td>
  </tr>
</table>

Table 1: Results on the validation data for WA-based XLT with various filtering strategies. Results with XLM-R and whitespace pre-tokenization (WS-TOK).

run all experiments with 3 random seeds and report the mean F1 score and standard deviation. We provide full training details in Appendix G.

4 Results and Discussion

Starting from the validation portions of our datasets, we assess the impact of low-level design choices related to using WA for token-level translation-based XLT (\S 4.1). Based on these findings, we compare WA—applying the on average best-performing pre-tokenization and filtering strategies—against two state-of-the-art marker-based methods, Easy and Codec, on the test portions of our datasets (\S 4.2). Last, we provide further ablations in \S 4.3, analyzing the impact of decoder-turned encoder LLMs, WA, and MT model on the XLT performance.

4.1 WA Design Choices

Filtering Strategies. Our preliminary experiments (Table 1) reveal that filtering has a negligible effect on performance in T-Train: on average none of the filtering strategies yields substantial gains. Still, COMP-INS and COMP-INS+COMP-TGT+COMP-SRC perform the best on average. This finding is positive, as searching for an optimal filtering strategy for T-Train is costly: it requires (re-)training language-specific models for every change in the filtering strategy. In stark contrast, applying the RSTR-TGT filter results in a substantial gain (+9.2% over NO-FILT) for T-Test. On average, we find RSTR-TGT to be the most successful strategy for T-Test. Adding additional filtering (+COMP-SRC) only results in marginal gains for Masakha.

Pre-Tokenization. Figure 2 shows the results for various pre-tokenization approaches. The results mirror the filtering findings: the pre-tokenization strategy has (1) little impact on the T-Train performance—language-agnostic whitespace pre-tokenization (WS-TOK) is marginally better than language-specific pre-tokenization (LS-TOK)—and (2) a substantial impact on T-Test—LS-TOK outperforms WS-TOK by 12.6% on Masakha and 6.0% on xSID. Our findings add to prior work (Artetxe et al., 2023; Ebing and Glavaš, 2024), which showed that T-Test is more affected by translation quality than T-Train: our results extend this finding to filtering and pre-tokenization strategies.

![Bar chart comparing transfer performance with WA for language-specific pre-tokenization (LS-TOK) relative to whitespace pre-tokenization (WS-TOK). Results with XLM-R, COMP-INS+COMP-TGT+COMP-SRC filtering for T-Train and RSTR-TGT filtering for T-Test.](page_1012_186_388_246.png)

Figure 2: Transfer performance with WA for language-specific pre-tokenization (LS-TOK) relative to whitespace pre-tokenization (WS-TOK). Results with XLM-R, COMP-INS+COMP-TGT+COMP-SRC filtering for T-Train and RSTR-TGT filtering for T-Test.

We now compare our optimized WA-based configurations for T-Train and T-Test against the state-of-the-art marker-based label projection methods Easy and Codec. For the remainder of experiments, we apply language-agnostic whitespace pre-tokenization (WS-TOK) and COMP-INS+COMP-TGT+COMP-SRC filtering for T-Train and language-specific pre-tokenization (LS-TOK) and RSTR-TGT filtering for T-Test.

Translate-Train. We find T-Train, regardless of the label projection strategy (WA, Easy, or Codec), to substantially outperform zero-shot XLT with the mLM (e.g., by 14.2% on Masakha for WA-based projection). Contrary to the results of prior work that reported translation-based XLT with WAs inferior to Easy (Chen et al., 2023) and Codec (Le et al., 2024), we demonstrate that—when reasonably configured—WA yields competitive performance: on xSID, WA-based T-Train lags marker-based transfer by less than 1%; and on Masakha WA-based transfer even slightly outperforms both Easy and Codec.
<table>
  <tr>
    <th></th>
    <th>Masakha</th>
    <th>xSID</th>
    <th>Avg</th>
  </tr>
  <tr>
    <th colspan="4">Zero-Shot</th>
  </tr>
  <tr>
    <td>X</td>
    <td>52.9±1.8</td>
    <td>76.8±1.4</td>
    <td>64.9±1.6</td>
  </tr>
  <tr>
    <th colspan="4">Translate-Train</th>
  </tr>
  <tr>
    <td>Easy X</td>
    <td>66.0±0.9</td>
    <td>83.6±0.9</td>
    <td>74.8±0.9</td>
  </tr>
  <tr>
    <td>Codec X</td>
    <td>66.9±1.6</td>
    <td>83.4±0.8</td>
    <td>75.2±1.3</td>
  </tr>
  <tr>
    <td>WA X</td>
    <td>67.1±1.2</td>
    <td>82.7±0.9</td>
    <td>74.9±1.0</td>
  </tr>
  <tr>
    <th colspan="4">Translate-Test</th>
  </tr>
  <tr>
    <td>Codec X</td>
    <td>72.0±0.5</td>
    <td>79.4±0.3</td>
    <td>75.7±0.4</td>
  </tr>
  <tr>
    <td>Codec D</td>
    <td>72.4±0.4</td>
    <td>79.5±0.4</td>
    <td>76.0±0.4</td>
  </tr>
  <tr>
    <td>WA X</td>
    <td>72.3±0.5</td>
    <td>80.2±0.3</td>
    <td>76.3±0.4</td>
  </tr>
  <tr>
    <td>WA D</td>
    <td>72.7±0.4</td>
    <td>80.2±0.4</td>
    <td>76.5±0.4</td>
  </tr>
  <tr>
    <th colspan="4">Ensemble-Train-Test</th>
  </tr>
  <tr>
    <td>Easy + WA X + D</td>
    <td>71.7±0.7</td>
    <td>83.8±0.8</td>
    <td>77.7±0.7</td>
  </tr>
  <tr>
    <td>Codec + WA X + D</td>
    <td>72.3±0.7</td>
    <td>82.8±0.8</td>
    <td>77.5±0.7</td>
  </tr>
  <tr>
    <td>WA + WA X + D</td>
    <td>72.6±0.6</td>
    <td>83.4±0.9</td>
    <td>78.0±0.8</td>
  </tr>
</table>

Table 2: Main results for translation-based XLT for token-level tasks. Results with XLM-R (X) and DeBERTa (D).

<table>
  <tr>
    <th></th>
    <th>Masakha</th>
    <th>xSID</th>
    <th>Avg</th>
  </tr>
  <tr>
    <th colspan="4">Translate-Test</th>
  </tr>
  <tr>
    <td>WS-TOK D</td>
    <td>59.8±0.3</td>
    <td>73.2±0.4</td>
    <td>66.0±0.4</td>
  </tr>
  <tr>
    <td>LS-TOK D</td>
    <td>72.7±0.4</td>
    <td>80.2±0.4</td>
    <td>76.5±0.4</td>
  </tr>
  <tr>
    <th colspan="4">Ensemble-Train-Test</th>
  </tr>
  <tr>
    <td>WS- + WS-TOK X + D</td>
    <td>72.0±0.8</td>
    <td>81.2±1.0</td>
    <td>76.6±0.9</td>
  </tr>
  <tr>
    <td>WS- + LS-TOK X + D</td>
    <td>72.6±0.6</td>
    <td>83.4±0.9</td>
    <td>78.0±0.8</td>
  </tr>
</table>

Table 3: Robustness results for ETT utilizing different pre-tokenizations for the T-Test component: whitespace (WS-TOK) and language-specific (LS-TOK). Results with XLM-R (X) and DeBERTa (D).

Translate-Test. Irrespective of the label projection approach (WA, Easy, or Codec), T-Test outperforms zero-shot XLT (and T-Train on Masakha). Again we observe that optimized WA-based label projection matches and even slightly surpasses the performance of the marker-based Codec. This is encouraging because the label projection with Codec—due to its two-step translation procedure—is computationally more expensive (i.e., slower) than WA. Further, we observe that models solely trained on English (i.e., DeBERTa) only offer marginal gains over comparable mLMs (i.e., XLM-R). We speculate that this is because NER and SL do not require advanced language understanding abilities and thus the monolingual English ability of an mLM suffices for these tasks.

Ensemble-Train-Test. On average, our proposed ensemble ETT improves over T-Train and T-Test by 3.1% and 1.5%, respectively. We summarize our observations as follows: (i) in scenarios where T-Train performs better than T-Test, ETT achieves additional gains over T-Train by leveraging the complementary strengths of T-Test; (ii) in scenarios where T-Train performance is worse than T-Test, utilizing ETT does not harm because it results in similar performance as T-Test.

Robustness via Ensembling. Our preliminary studies on WA-related low-level design choices (§4.1) revealed notable performance variation, especially for T-Test. We now show that our proposed ensemble ETT not only improves performance over both T-Train and T-Test but also reduces sensitivity to design details of WA. Table 3 compares T-Test and ETT with WA-based label projection for the two pre-tokenization strategies (WS-TOK and LS-TOK). For ETT, we modify the pre-tokenization only for the T-Test part of the ensemble and keep the T-Train pre-tokenization unchanged. We observe that for T-Test, WS-TOK underperforms LS-TOK by 9.9% on average. ETT, however, almost completely closes the gap between the two, with WS-TOK trailing LS-TOK by only 1.4%, making the choice of the pre-tokenizer much less consequential for the final performance.

4.3 Further Findings

LLMs as Encoders. Prior work rendered decoder-only LLMs inferior to smaller encoder-only models for token-level tasks (Ahuja et al., 2023; Le et al., 2024; Dukić and Snajder, 2024), but more recent efforts suggest that autoregressive LLMs can be post-hoc turned into competitive bidirectional encoders (BehnamGhader et al., 2024; Wang et al., 2024). We thus evaluate a state-of-the-art decoder-turned-encoder Llama 3 8B (LLM2Vec) (BehnamGhader et al., 2024) in translation-based XLT for token classification. Following the original work, we add a linear classifier with dropout on top of LLM2Vec, fine-tuning only the classifier. Figure 3 summarizes the results. We observe that much smaller DeBERTa is superior to LLM2Vec in T-Test (i.e., +12.4% on Masakha and 9.9% on xSID) and that T-Test with LLM2Vec even trails zero-shot transfer with XLM-R on xSID. In ETT with an XLM-R-based T-Train component, LLM2Vec becomes much more competitive and lags DeBERTa by only 1.5% on average, further emphasizing the robustness that our ETT ensembling brings to translation-based XLT for token-level tasks.

Choice of Word Aligner. We next ablate the im-
Figure 3: Results for translation-based XLT with LLM2Vec (L) vs. DeBERTa (D), relative to zero-shot XLT performance with XLM-R (X).

<table>
  <tr>
    <th></th>
    <th>Masakha</th>
    <th>xSID</th>
    <th>Avg</th>
  </tr>
  <tr>
    <th colspan="4">Translate-Train</th>
  </tr>
  <tr>
    <td>AccAlign</td>
    <td>67.1<sub>±1.2</sub></td>
    <td>82.7<sub>±0.8</sub></td>
    <td>75.0<sub>±1.0</sub></td>
  </tr>
  <tr>
    <td>AccAlign<sub>noft</sub></td>
    <td>66.7<sub>±1.1</sub></td>
    <td>82.9<sub>±0.5</sub></td>
    <td>74.9<sub>±1.0</sub></td>
  </tr>
  <tr>
    <td>Awesome<sub>noft</sub></td>
    <td>64.4<sub>±1.3</sub></td>
    <td>79.8<sub>±0.8</sub></td>
    <td>72.7<sub>±1.2</sub></td>
  </tr>
  <tr>
    <th colspan="4">Translate-Test</th>
  </tr>
  <tr>
    <td>AccAlign</td>
    <td><b>72.3</b><sub>±0.5</sub></td>
    <td>80.2<sub>±0.8</sub></td>
    <td><b>76.3</b><sub>±0.4</sub></td>
  </tr>
  <tr>
    <td>AccAlign<sub>noft</sub></td>
    <td>70.4<sub>±0.5</sub></td>
    <td>79.6<sub>±0.3</sub></td>
    <td>75.0<sub>±0.4</sub></td>
  </tr>
  <tr>
    <td>Awesome<sub>noft</sub></td>
    <td>65.1<sub>±0.4</sub></td>
    <td>74.8<sub>±0.3</sub></td>
    <td>70.0<sub>±0.4</sub></td>
  </tr>
</table>

Table 4: Comparison of translation-based XLT with different WAs; noft denotes vanilla WAs without fine-tuning. Results with XLM-R

<table>
  <tr>
    <th></th>
    <th>Masakha</th>
    <th>xSID</th>
    <th>Avg</th>
  </tr>
  <tr>
    <th colspan="4">Translate-Test</th>
  </tr>
  <tr>
    <td>NLLB</td>
    <td>D</td>
    <td>72.6<sub>±0.4</sub></td>
    <td>80.2<sub>±0.4</sub></td>
    <td>76.4<sub>±0.4</sub></td>
  </tr>
  <tr>
    <td>GT</td>
    <td>D</td>
    <td><b>74.9</b><sub>±0.4</sub></td>
    <td>81.2<sub>±0.4</sub></td>
    <td><b>78.1</b><sub>±0.4</sub></td>
  </tr>
  <tr>
    <th colspan="4">Ensemble-Train-Test</th>
  </tr>
  <tr>
    <td>NLLB + NLLB</td>
    <td>X + D</td>
    <td>71.9<sub>±0.6</sub></td>
    <td><b>83.4</b><sub>±0.9</sub></td>
    <td>77.7<sub>±0.8</sub></td>
  </tr>
  <tr>
    <td>NLLB + GT</td>
    <td>X + D</td>
    <td>73.0<sub>±0.6</sub></td>
    <td>83.2<sub>±0.9</sub></td>
    <td><b>78.1</b><sub>±0.8</sub></td>
  </tr>
</table>

Table 5: Results for translation-based XLT using different MT models for T-Test: NLLB and Google Translate (GT). Results with XLM-R (X) and DeBERTa (D). Scores for Masakha differ as GT does not support all languages.

Translation Quality. Commercial MT models are typically considered to produce superior translation quality compared to their publicly available counterparts. To evaluate the impact of the MT model on token-level translation-based XLT, we generate translations using Google Translate (GT), which serves as a representative example of a commercial MT model. We report results for T-Test and ETT only (Table 5) as prior work already demonstrated that translation quality has a less pronounced impact on T-Train (Artetxe et al., 2023; Ebings and Glavaš, 2024). For T-Test, we find that GT outperforms NLLB by 1.7% on average. Nevertheless, the gains obtained by a more powerful MT model are on par with the performance improvements introduced by using our ensemble (ETT) with NLLB only. Additionally, the difference in ETT performance between GT and NLLB is negligible (0.4%), which once more points to the robustness that ETT brings to XLT for token classification tasks.

5 Conclusion

In this work, we thoroughly investigated the role of word aligners (WAs) in translation-based crosslingual transfer for token classification tasks. Our evaluation on two established benchmarks covering 29 languages, revealed that low-level design decisions related to label projection via WA can have a substantial effect on translation-based XLT strategies, in particular translate-test. We then showed that an optimized application of WA-based label projection can match or even surpass the transfer performance of recent marker-based approaches (Chen et al., 2023; Le et al., 2024), contrary to their findings. Further, we proposed a more sophisticated WA-based transfer approach that ensembles predictions of translate-train and translate-test. We demonstrated that the proposed ensemble not only
substantially increases transfer performance but also reduces the sensitivity to low-level design decisions of WA-based label projection.

6 Limitations

We focused on systematically exploring the design choices relevant for translation-based XLT using WA. However, our study is limited by the prevalent practice of creating new evaluation datasets by translating the data from an existing high-resource language to the desired (new) language. This applies to xSID and some languages of Masakha. The resulting data may contain distinct characteristics that stem from the translation process often referred to as translationese. Prior work (Artetxe et al., 2020) stated that translation-based XLT strategies might lead to the exploitation of translationese, slightly overestimating the true performance.

For the proposed filtering strategies, we strove for simple and task-agnostic designs. We are aware that the presented filtering strategies are only an excerpt from the endless search space of possible options. However, the contribution of our work does not focus on fully exploring the design space of filtering strategies but demonstrating that filtering strategies for WAs may have a substantial impact on the XLT performance. Further, we show that our proposed ensemble successfully mitigates the observed performance variations making suboptimal filtering strategies less influential for the final XLT performance.

Our span mapping approach assumes that the labeled spans projected from the source language sentence \( S \) to the target language sentence \( \hat{T} \) for T-Train (or \( \hat{S} \) to \( T \) for T-Test) are continuous—such that a single labeled span from the source language sentence may not be split into multiple labeled spans in the target language sentence. This is an (overly) simplifying assumption that might not hold for all pairs of source-to-target (or target-to-source) translations. In our evaluation, the assumption does not hold for some German and Dutch examples in the xSID dataset because the languages allow for sentence final verbs. Therefore, a single slot may be split into discontinuous labeled spans during translation. Nevertheless, our empirical evaluation shows strong results even for German and Dutch indicating that the appearance of discontinuous labeled spans is not too common.

Acknowledgements

Simulations were performed with computing resources from Julia 2. Julia 2 was funded as DFG project as “Forschungsgroßgerät nach Art 91b GG” under INST 93/1145-1 FUGG”

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A Translation Data

For Masakha (Adelani et al., 2022) and xSID (van der Goot et al., 2021), we concatenated the pre-tokenized input on whitespace before translation. We deviate from this for the Chinese data in xSID, where we merge Chinese tokens without whitespace. Additionally, the dialect South Tyrol (de-st) in xSID is not supported by NLLB. We translate the dialect pretending it to be German (i.e., using the German language code) as it is closely related to the latter. Further, the Serbian (sr) data in xSID is written in Latin script, whereas NLLB was only trained in Serbian Cyrillic script. We accessed all datasets through the Hugging Face library and ensured compliance with the licenses.

B Word Alignment

For our main experiments, we use the neural word aligner AccAlign (Wang et al., 2022), accessed through the following repository: https://github.com/sufenlp/AccAlign. Additionally, we employ Awesome (Dou and Neubig, 2021) with the code provided in the following repository: https://github.com/neulab/awesome-align. We follow the hyperparameter configuration proposed by the authors. We ensure compliance with the license for Awesome (BSD 3-Clause). We could not find licensing information for AccAlign.

C Easy

The code and data of Easy is released under the MIT license. We used the publicly released data for Masakha. For xSID, we produced our own translated data by adopting the existing code. We followed their implementation for Masakha closely. Easy (Chen et al., 2023) requires fine-tuning NLLB for preserving inserted tags (i.e., preserving "[l" and "]") around labeled spans). Hence, we leverage their publicly released 3.3B parameter checkpoint from Chen et al. (2023) for translation. We accessed it through the Hugging Face library.

D Codec

The authors of Codec did not release the translated data but published the source code instead. We created our own translated data for Masakha following their implementation. Further, we extended their implementation to produce the translated data for xSID. We adhered to the hyperparameters in their repository and followed the existing implementation closely. The translations for Codec are
obtained using vanilla (i.e., non fine-tuned) NLLB. However, the constrained decoding (i.e., inserting the tags post-translation) requires a fine-tuned NLLB that is able to preserve/insert tags. Therefore, for constrained decoding, we follow Le et al. (2024) using the fine-tuned 600M parameter version of NLLB released by Chen et al. (2023). We could not find licensing information for Codec.

E T-Projection

T-Projection (T-Proj) (García-Ferrero et al., 2023) solves the task of identifying the labeled spans in the translated target language sentence as a sequence-to-sequence problem. In the first step, the translated target language sentence is obtained by machine-translating it from the original source sentence. Next, the translated sentence and the type of the labeled span (i.e., LOC in NER) are fed to a sequence-to-sequence model that generates multiple candidates for the corresponding labeled span in the translated sentence. Finally, the best candidate is chosen by computing a translation quality metric between the labeled span from the original source language sentence and the candidates produced by the sequence-to-sequence model. For our preliminary results shown in Appendix M, we reuse the data for Masakha released by García-Ferrero et al. (2023) in the following repository: https://github.com/ikergarcia1996/T-Projection. We ensure compliance with the T-Proj license (Apache-2.0).

F CLaP

CLaP (Parekh et al., 2024) leverages contextualized translation to map the labeled spans from the original to the translated sentence. First, the original sentence is machine-translated to the target language. Second, an LLM is prompted to perform contextualized translation: given the translated target language sentence and the labeled spans of the original sentence, the LLM is prompted to output the labeled spans in the translated target language sentence. For our preliminary results shown in Appendix M, we reuse the implementation of Parekh et al. (2024) provided in the following repository: https://github.com/PlusLabNLP/CLaP. Furthermore, we follow their experimental setup closely. We use the text completion version of Llama-2 (Touvron et al., 2023) with 13B parameters. Together with their prompt template, we provide two in-context examples per language. We create these examples with GPT-4o mini (OpenAI et al., 2024) using the same prompt template used for CLaP. We manually validate the correctness of the translated target language entities by back-translating them with GT. We could not find licensing information for CLaP.

G Detailed Experimental Setup

We train both tasks (NER and SL) for 10 epochs using an effective batch size of 32. In case we can not fit the desired batch size, we utilize gradient accumulation. The learning rate is set to \(1e^{-5}\) with a weight decay of 0.01. We implement a linear schedule of 10% warm-up and employ mixed precision. For the LLM2Vec experiments, we deviate from this setting as we only fine-tune the classifier. Following BehnamGhader et al. (2024), we set the learning rate to \(5e^{-4}\). We evaluate models at the last checkpoint of training. We use the seqeval F1 implementation accessed through the Hugging Face library. Further, we access our downstream models—XLM-RoBERTa Large, DeBERTaV3 Large, and LLM2Vec Llama 3 8B Instruct MNTP—through the Hugging Face library. All translations were run on a single A100 with 40GB VRAM, and all downstream training and evaluation runs were completed on a single V100 with 32GB VRAM. We estimate the GPU time to 4000 hours across all translations and downstream finetunings.

Languages.

MasakhaNER2.0. Our experiments cover 18 out of 20 languages that are supported by NLLB. Note that Google Translate (GT) does not support all 18 languages. Following, we mark the 11 languages that are supported by GT with an additional asterisk: Bambara (bam)*, Ewé (ewe)*, Fon (fon), Hausa (hau)*, Igbo (ibo)*, Kinyarwanda (kin)*, Luganda (lug), Luo (luo), Mossi (most), Chichewa (nya), chiShona (sna)*, Kiswahili (saw)*, Setswana (tsn), Akan/Twi (twi)*, Wolof (wol), isiXhosa (xho)*, Yorùbá (yor)*, and isiZulu (zul)*.

xSID. We evaluate 11 languages all covered by NLLB and GT: Arabic (ar), Danish (da), German (de), South-Tyrolean (de-st), Indonesian (id), Italian (it), Kazakh (kk), Dutch (nl), Serbian (sr), Turkish (tr), and Chinese (zh). Following Razmovskaia et al. (2023), we excluded Japanese from the evaluation because it only has half of the validation and test instances and spans only a fraction of entities compared to the other languages.
Filtering Strategy. We use a greedy approach to explore the various design options (§4.1). We start with the selection of the filtering strategy, followed by our pre-tokenization experiments. For the exploration of the filtering strategy, we apply whitespace pre-tokenization (WS-TOK).

Pre-Tokenization. For the per-tokenization experiments, we filter the translated training data based on COMP-INS+COMP-SRC+COMP-TGT. For T-Test, we apply RSTR-TGT. Language-specific tokenization (LS-TOK) is done with the MosesTokenizer from the Sacremoses library (https://github.com/hplgit-project/sacremoses), except for Chinese, where we use jieba (https://github.com/fxsjy/jieba). Both tokenizers are released under the MIT license.

Main Results and Further Findings. As suggested by the findings of our preliminary experiments, we apply whitespace pre-tokenization (WS-TOK) for T-Train, except for Chinese in xSID, where we use language-specific tokenization (LS-TOK). For T-Test, we use language-specific pre-tokenization (LS-TOK). We utilize the same filtering as for the pre-tokenization experiments: COMP-INS+COMP-SRC+COMP-TGT for T-Train and RSTR-TGT for T-Test.

Ensembling with Codec as T-Test Component. In our main results (see Table 2), we report ETT only with a WA-based T-Test component while generally, the ensembling approach is not limited to that. For the T-Test component, an explicit mapping between the \( m \) labeled spans from the translated sentence to the \( n \) labeled spans in the original sentence is required such that the logits can be projected from the translated to the original sentence. The current implementation of Codec does not explicitly provide this mapping since it only outputs the labeled translated sentence. Obtaining the mapping would require a non-trivial extension of the implementation of Codec. Nevertheless, we argue that if the extension is implemented, there is no reason why ETT with a Codec T-Test component should perform worse than with a WA T-Test component. However, we do not expect it to perform better either since the individual T-Train and T-Test performance of Codec and our WA-based approach is comparable.
H  Detailed Results: Filtering Strategies

<table>
  <tr>
    <th></th>
    <th>bam</th>
    <th>cwe</th>
    <th>fon</th>
    <th>hau</th>
    <th>ibo</th>
    <th>kin</th>
    <th>lug</th>
    <th>luo</th>
    <th>mos</th>
    <th>nya</th>
    <th>sna</th>
    <th>swa</th>
    <th>tsn</th>
    <th>twi</th>
    <th>wol</th>
    <th>xho</th>
    <th>yor</th>
    <th>zul</th>
    <th>Avg</th>
  </tr>
  <tr>
    <th colspan="21">Translate-Train</th>
  </tr>
  <tr>
    <td>NO-FILT</td><td>43.8</td><td>78.4</td><td>72.9</td><td>66.7</td><td>62.9</td><td>70.2</td><td>79.6</td><td>69.7</td><td>58.9</td><td>65.2</td><td>72.5</td><td>84.4</td><td>69.1</td><td>57.3</td><td>63.3</td><td>63.2</td><td>33.7</td><td>67.1</td><td>65.5</td>
  </tr>
  <tr>
    <td>COMP-INS</td><td>45.5</td><td>78.4</td><td>76.0</td><td>66.5</td><td>62.8</td><td>69.5</td><td>79.4</td><td>70.2</td><td>59.9</td><td>66.1</td><td>73.1</td><td>84.1</td><td>69.0</td><td>56.6</td><td>65.8</td><td>62.8</td><td>32.9</td><td>66.5</td><td>65.8</td>
  </tr>
  <tr>
    <td>COMP-INS + COMP-TGT</td><td>50.9</td><td>77.8</td><td>76.0</td><td>66.5</td><td>62.5</td><td>69.0</td><td>80.4</td><td>70.4</td><td>60.4</td><td>65.4</td><td>73.0</td><td>83.6</td><td>68.9</td><td>55.9</td><td>67.0</td><td>63.1</td><td>33.2</td><td>64.7</td><td>66.0</td>
  </tr>
  <tr>
    <td>COMP-INS + COMP-TGT + COMP-SRC</td><td>51.2</td><td>78.8</td><td>76.5</td><td>66.5</td><td>63.7</td><td>69.7</td><td>80.0</td><td>70.0</td><td>60.3</td><td>66.2</td><td>73.1</td><td>83.6</td><td>68.2</td><td>60.8</td><td>67.3</td><td>63.4</td><td>32.6</td><td>66.3</td><td>66.6</td>
  </tr>
  <tr>
    <th colspan="21">Translate-Test</th>
  </tr>
  <tr>
    <td>NO-FILT</td><td>26.2</td><td>49.0</td><td>40.0</td><td>55.5</td><td>43.3</td><td>55.7</td><td>58.6</td><td>53.9</td><td>36.6</td><td>51.9</td><td>52.6</td><td>55.3</td><td>52.6</td><td>54.3</td><td>42.9</td><td>30.9</td><td>29.0</td><td>45.7</td><td>46.3</td>
  </tr>
  <tr>
    <td>RSTR-TGT</td><td>37.6</td><td>68.0</td><td>58.0</td><td>61.7</td><td>55.5</td><td>67.1</td><td>70.9</td><td>61.6</td><td>46.4</td><td>64.7</td><td>60.9</td><td>67.4</td><td>63.2</td><td>61.6</td><td>55.6</td><td>43.1</td><td>43.0</td><td>55.5</td><td>57.9</td>
  </tr>
  <tr>
    <td>COMP-TGT</td><td>32.5</td><td>52.0</td><td>47.2</td><td>56.9</td><td>44.5</td><td>56.8</td><td>59.7</td><td>55.2</td><td>40.6</td><td>52.9</td><td>53.0</td><td>55.7</td><td>54.1</td><td>59.6</td><td>46.0</td><td>31.5</td><td>32.8</td><td>46.2</td><td>48.7</td>
  </tr>
  <tr>
    <td>COMP-SRC</td><td>28.9</td><td>47.7</td><td>41.5</td><td>56.3</td><td>43.4</td><td>55.6</td><td>58.8</td><td>54.0</td><td>35.1</td><td>52.1</td><td>52.7</td><td>54.5</td><td>51.8</td><td>56.4</td><td>44.2</td><td>30.8</td><td>28.7</td><td>45.9</td><td>46.6</td>
  </tr>
  <tr>
    <td>COMP-TGT + COMP-SRC</td><td>34.4</td><td>50.5</td><td>47.9</td><td>57.4</td><td>44.6</td><td>56.7</td><td>59.8</td><td>55.7</td><td>38.8</td><td>53.0</td><td>53.0</td><td>54.8</td><td>53.1</td><td>61.1</td><td>46.5</td><td>31.4</td><td>32.7</td><td>46.4</td><td>48.8</td>
  </tr>
  <tr>
    <td>RSTR-TGT + COMP-TGT</td><td>36.8</td><td>67.7</td><td>52.4</td><td>62.9</td><td>55.9</td><td>66.8</td><td>71.6</td><td>62.5</td><td>45.0</td><td>65.0</td><td>61.0</td><td>68.0</td><td>64.2</td><td>61.1</td><td>57.0</td><td>43.6</td><td>43.4</td><td>55.6</td><td>57.8</td>
  </tr>
  <tr>
    <td>RSTR-TGT + COMP-SRC</td><td>41.7</td><td>68.2</td><td>60.5</td><td>62.6</td><td>55.8</td><td>67.0</td><td>71.2</td><td>63.0</td><td>45.4</td><td>65.0</td><td>61.1</td><td>66.8</td><td>62.6</td><td>64.0</td><td>57.5</td><td>43.1</td><td>42.9</td><td>55.7</td><td>58.6</td>
  </tr>
  <tr>
    <td>RSTR-TGT + COMP-TGT + COMP-SRC</td><td>39.1</td><td>66.6</td><td>53.2</td><td>63.5</td><td>56.1</td><td>66.7</td><td>71.7</td><td>63.1</td><td>43.4</td><td>65.1</td><td>61.1</td><td>67.2</td><td>63.3</td><td>62.7</td><td>57.8</td><td>43.5</td><td>43.4</td><td>55.8</td><td>58.0</td>
  </tr>
</table>

Table 6: Results for translation-based XLT evaluated on the Masakha validation data utilizing different filtering strategies. We use XLM-R.

<table>
  <tr>
    <th></th>
    <th>ar</th>
    <th>da</th>
    <th>de</th>
    <th>de-st</th>
    <th>id</th>
    <th>it</th>
    <th>kk</th>
    <th>nl</th>
    <th>sr</th>
    <th>tr</th>
    <th>zh</th>
    <th>Avg</th>
  </tr>
  <tr>
    <th colspan="12">Translate-Train</th>
  </tr>
  <tr>
    <td>NO-FILT</td><td>85.4</td><td>81.8</td><td>88.3</td><td>60.3</td><td>86.2</td><td>89.8</td><td>70.5</td><td>94.1</td><td>85.7</td><td>85.9</td><td>-</td><td>82.8</td>
  </tr>
  <tr>
    <td>COMP-INS</td><td>85.1</td><td>82.4</td><td>88.7</td><td>58.3</td><td>86.0</td><td>90.1</td><td>70.7</td><td>93.3</td><td>84.6</td><td>88.0</td><td>-</td><td>82.7</td>
  </tr>
  <tr>
    <td>COMP-INS + COMP-TGT</td><td>84.9</td><td>81.9</td><td>87.8</td><td>59.3</td><td>85.3</td><td>86.7</td><td>69.8</td><td>90.9</td><td>86.6</td><td>86.6</td><td>-</td><td>82.0</td>
  </tr>
  <tr>
    <td>COMP-INS + COMP-TGT + COMP-SRC</td><td>86.3</td><td>81.3</td><td>86.7</td><td>58.8</td><td>85.0</td><td>88.1</td><td>69.6</td><td>91.3</td><td>86.2</td><td>86.5</td><td>-</td><td>82.0</td>
  </tr>
  <tr>
    <th colspan="12">Translate-Test</th>
  </tr>
  <tr>
    <td>NO-FILT</td><td>68.1</td><td>75.6</td><td>74.4</td><td>51.9</td><td>73.1</td><td>76.6</td><td>51.7</td><td>80.2</td><td>69.9</td><td>66.6</td><td>61.6</td><td>68.2</td>
  </tr>
  <tr>
    <td>RSTR-TGT</td><td>73.6</td><td>78.1</td><td>82.7</td><td>57.3</td><td>73.5</td><td>83.4</td><td>61.7</td><td>86.4</td><td>75.9</td><td>75.1</td><td>75.4</td><td>74.8</td>
  </tr>
  <tr>
    <td>COMP-TGT</td><td>68.4</td><td>75.5</td><td>79.4</td><td>53.8</td><td>74.3</td><td>75.5</td><td>59.8</td><td>79.8</td><td>70.6</td><td>74.9</td><td>74.6</td><td>71.5</td>
  </tr>
  <tr>
    <td>COMP-SRC</td><td>67.2</td><td>74.3</td><td>73.4</td><td>51.7</td><td>71.5</td><td>75.5</td><td>50.4</td><td>78.9</td><td>68.2</td><td>65.2</td><td>61.0</td><td>67.0</td>
  </tr>
  <tr>
    <td>COMP-TGT + COMP-SRC</td><td>67.4</td><td>74.3</td><td>78.8</td><td>53.4</td><td>72.8</td><td>74.8</td><td>59.0</td><td>78.9</td><td>68.7</td><td>72.8</td><td>73.5</td><td>70.4</td>
  </tr>
  <tr>
    <td>RSTR-TGT + COMP-TGT</td><td>74.1</td><td>77.9</td><td>81.7</td><td>57.6</td><td>74.0</td><td>82.1</td><td>60.1</td><td>84.6</td><td>76.3</td><td>73.8</td><td>74.9</td><td>74.3</td>
  </tr>
  <tr>
    <td>RSTR-TGT + COMP-SRC</td><td>72.9</td><td>77.0</td><td>82.0</td><td>57.2</td><td>71.9</td><td>82.5</td><td>60.8</td><td>85.2</td><td>74.3</td><td>73.9</td><td>75.1</td><td>73.9</td>
  </tr>
  <tr>
    <td>RSTR-TGT + COMP-TGT + COMP-SRC</td><td>73.3</td><td>76.7</td><td>81.1</td><td>57.3</td><td>72.4</td><td>81.6</td><td>59.3</td><td>83.8</td><td>74.5</td><td>71.7</td><td>73.8</td><td>73.2</td>
  </tr>
</table>

Table 7: Results for translation-based XLT evaluated on the xSID validation data utilizing different filtering strategies. We use XLM-R. For T–Train, we excluded Chinese (zh) since experiments were run with whitespace pre-tokenization (WS–TOK).

I  Filtering Strategies: Recovered Instances for Translate-Train

<table>
  <tr>
    <th></th>
    <th>bam</th>
    <th>cwe</th>
    <th>fon</th>
    <th>hau</th>
    <th>ibo</th>
    <th>kin</th>
    <th>lug</th>
    <th>luo</th>
    <th>mos</th>
    <th>nya</th>
    <th>sna</th>
    <th>swa</th>
    <th>tsn</th>
    <th>twi</th>
    <th>wol</th>
    <th>xho</th>
    <th>yor</th>
    <th>zul</th>
    <th>Avg</th>
  </tr>
  <tr>
    <td>COMP-INS</td><td>97.5</td><td>98.4</td><td>95.0</td><td>99.5</td><td>99.2</td><td>98.4</td><td>98.5</td><td>98.3</td><td>93.7</td><td>99.3</td><td>99.5</td><td>99.7</td><td>99.2</td><td>98.5</td><td>96.0</td><td>99.0</td><td>98.7</td><td>99.3</td><td>98.2</td>
  </tr>
  <tr>
    <td>COMP-INS + COMP-TGT</td><td>96.5</td><td>97.0</td><td>94.1</td><td>96.7</td><td>98.0</td><td>92.6</td><td>97.2</td><td>97.0</td><td>92.3</td><td>96.8</td><td>99.3</td><td>94.5</td><td>96.5</td><td>97.7</td><td>94.3</td><td>98.6</td><td>97.2</td><td>98.9</td><td>96.4</td>
  </tr>
  <tr>
    <td>COMP-INS + COMP-TGT + COMP-SRC</td><td>93.6</td><td>94.7</td><td>92.2</td><td>95.0</td><td>97.3</td><td>91.6</td><td>95.9</td><td>95.6</td><td>89.2</td><td>96.1</td><td>98.6</td><td>93.9</td><td>95.7</td><td>95.8</td><td>91.6</td><td>97.4</td><td>96.0</td><td>97.3</td><td>94.9</td>
  </tr>
</table>

Table 8: Relative number of recovered instances on the Masakha validation data utilizing different filtering strategies.

<table>
  <tr>
    <th></th>
    <th>ar</th>
    <th>da</th>
    <th>de</th>
    <th>de-st</th>
    <th>id</th>
    <th>it</th>
    <th>kk</th>
    <th>nl</th>
    <th>sr</th>
    <th>tr</th>
    <th>zh</th>
    <th>Avg</th>
  </tr>
  <tr>
    <td>COMP-INS</td><td>93.5</td><td>97.9</td><td>96.4</td><td>98.4</td><td>98.2</td><td>97.8</td><td>90.6</td><td>96.6</td><td>97.2</td><td>92.0</td><td>-</td><td>95.7</td>
  </tr>
  <tr>
    <td>COMP-INS + COMP-TGT</td><td>85.9</td><td>92.4</td><td>85.4</td><td>85.4</td><td>92.1</td><td>78.6</td><td>81.6</td><td>84.4</td><td>91.5</td><td>87.0</td><td>-</td><td>86.4</td>
  </tr>
  <tr>
    <td>COMP-INS + COMP-TGT + COMP-SRC</td><td>70.8</td><td>82.3</td><td>78.5</td><td>78.5</td><td>83.1</td><td>71.0</td><td>65.9</td><td>79.5</td><td>75.7</td><td>71.9</td><td>-</td><td>75.7</td>
  </tr>
</table>

Table 9: Relative number of recovered instances on the xSID validation data utilizing different filtering strategies. We excluded Chinese (zh) since filtering was run with whitespace pre-tokenization (WS–TOK).
J  Filtering Strategies: Mapped Labeled Spans for Translate-Test

<table>
  <tr>
    <th></th>
    <th>bam</th>
    <th>ewe</th>
    <th>fon</th>
    <th>hau</th>
    <th>ibo</th>
    <th>kin</th>
    <th>lug</th>
    <th>luo</th>
    <th>mos</th>
    <th>nya</th>
    <th>sna</th>
    <th>swa</th>
    <th>tsn</th>
    <th>twi</th>
    <th>wol</th>
    <th>yor</th>
    <th>zul</th>
    <th>Avg</th>
  </tr>
  <tr>
    <td>RSTR-TGT</td>
    <td>101.0</td><td>100.5</td><td>100.3</td><td>100.1</td><td>100.2</td><td>100.0</td><td>100.1</td><td>100.1</td><td>100.4</td><td>100.1</td><td>100.2</td><td>100.1</td><td>100.2</td><td>100.1</td><td>100.1</td><td>101.4</td><td>100.3</td><td>100.3</td>
  </tr>
  <tr>
    <td>RSTR-TGT + COMP-TGT</td>
    <td>70.5</td><td>87.4</td><td>71.8</td><td>92.1</td><td>94.7</td><td>94.1</td><td>95.3</td><td>92.7</td><td>79.8</td><td>96.7</td><td>99.1</td><td>88.6</td><td>91.4</td><td>81.7</td><td>86.6</td><td>97.9</td><td>86.8</td><td>98.6</td><td>89.2</td>
  </tr>
  <tr>
    <td>RSTR-TGT + COMP-SRC</td>
    <td>82.8</td><td>94.7</td><td>90.7</td><td>97.0</td><td>98.7</td><td>99.4</td><td>98.9</td><td>92.9</td><td>89.6</td><td>99.0</td><td>98.6</td><td>97.0</td><td>96.6</td><td>91.6</td><td>90.9</td><td>99.0</td><td>98.0</td><td>99.6</td><td>95.3</td>
  </tr>
  <tr>
    <td>RSTR-TGT + COMP-TGT + COMP-SRC</td>
    <td>60.1</td><td>82.7</td><td>67.2</td><td>90.2</td><td>93.4</td><td>93.7</td><td>94.5</td><td>87.6</td><td>72.3</td><td>95.9</td><td>97.8</td><td>86.6</td><td>89.2</td><td>76.4</td><td>81.7</td><td>97.1</td><td>84.1</td><td>97.8</td><td>86.0</td>
  </tr>
</table>

Table 10: Fraction of mapped labeled spans for the Masakha validation data relative to NO–FILT utilizing different filtering strategies. Filtering strategies might prevent interference between labeled spans of the same instance resulting in more labeled spans than in NO–FILT.

<table>
  <tr>
    <th></th>
    <th>ar</th>
    <th>da</th>
    <th>de</th>
    <th>de-st</th>
    <th>id</th>
    <th>it</th>
    <th>kk</th>
    <th>nl</th>
    <th>sr</th>
    <th>tr</th>
    <th>zh</th>
    <th>Avg</th>
  </tr>
  <tr>
    <td>RSTR-TGT</td>
    <td>100.2</td><td>100.2</td><td>100.4</td><td>103.7</td><td>100.6</td><td>100.2</td><td>109.2</td><td>100.9</td><td>100.2</td><td>108.6</td><td>112.4</td><td>102.4</td>
  </tr>
  <tr>
    <td>RSTR-TGT + COMP-TGT</td>
    <td>96.8</td><td>97.3</td><td>93.0</td><td>93.0</td><td>96.5</td><td>94.8</td><td>95.6</td><td>95.0</td><td>95.3</td><td>95.8</td><td>98.2</td><td>95.3</td>
  </tr>
  <tr>
    <td>RSTR-TGT + COMP-SRC</td>
    <td>93.7</td><td>95.3</td><td>96.3</td><td>98.7</td><td>96.4</td><td>95.2</td><td>103.8</td><td>97.4</td><td>95.2</td><td>102.0</td><td>108.2</td><td>97.4</td>
  </tr>
  <tr>
    <td>RSTR-TGT + COMP-TGT + COMP-SRC</td>
    <td>90.8</td><td>93.1</td><td>90.3</td><td>90.0</td><td>92.8</td><td>91.3</td><td>92.1</td><td>93.2</td><td>90.7</td><td>90.4</td><td>94.2</td><td>91.5</td>
  </tr>
</table>

Table 11: Fraction of mapped labeled spans for the xSID validation data relative to NO–FILT utilizing different filtering strategies. Filtering strategies might prevent interference between labeled spans of the same instance resulting in more labeled spans than in NO–FILT.

K  Detailed Results: Pre-Tokenization

<table>
  <tr>
    <th></th>
    <th>bam</th>
    <th>ewe</th>
    <th>fon</th>
    <th>hau</th>
    <th>ibo</th>
    <th>kin</th>
    <th>lug</th>
    <th>luo</th>
    <th>mos</th>
    <th>nya</th>
    <th>sna</th>
    <th>swa</th>
    <th>tsn</th>
    <th>twi</th>
    <th>wol</th>
    <th>xho</th>
    <th>yor</th>
    <th>zul</th>
    <th>Avg</th>
  </tr>
  <tr>
    <th colspan="20">Translate-Train</th>
  </tr>
  <tr>
    <td>WS-TOK</td>
    <td>51.2</td><td>78.8</td><td>76.5</td><td>66.5</td><td>63.7</td><td>69.7</td><td>80.0</td><td>70.0</td><td>60.3</td><td>66.2</td><td>73.1</td><td>83.6</td><td>68.2</td><td>60.8</td><td>67.3</td><td>63.4</td><td>32.6</td><td>66.3</td><td>66.6</td>
  </tr>
  <tr>
    <td>LS-TOK</td>
    <td>49.4</td><td>77.9</td><td>75.3</td><td>66.7</td><td>58.4</td><td>68.8</td><td>80.2</td><td>70.7</td><td>61.3</td><td>65.9</td><td>73.7</td><td>83.4</td><td>69.8</td><td>59.2</td><td>66.9</td><td>63.8</td><td>32.3</td><td>66.8</td><td>66.1</td>
  </tr>
  <tr>
    <th colspan="20">Translate-Test</th>
  </tr>
  <tr>
    <td>WS-TOK</td>
    <td>37.6</td><td>68.9</td><td>58.0</td><td>61.7</td><td>55.5</td><td>67.1</td><td>70.9</td><td>61.6</td><td>46.4</td><td>64.7</td><td>60.9</td><td>67.4</td><td>63.2</td><td>61.6</td><td>55.6</td><td>43.1</td><td>43.0</td><td>55.5</td><td>57.9</td>
  </tr>
  <tr>
    <td>LS-TOK</td>
    <td>50.8</td><td>82.5</td><td>74.8</td><td>67.0</td><td>71.5</td><td>78.8</td><td>86.8</td><td>76.2</td><td>55.2</td><td>75.8</td><td>82.0</td><td>81.9</td><td>76.1</td><td>68.8</td><td>66.1</td><td>62.9</td><td>54.7</td><td>69.4</td><td>71.2</td>
  </tr>
</table>

Table 12: Results for translation-based XLT evaluated on the Masakha validation data utilizing different pre-tokenization strategies. We use XLM-R.

<table>
  <tr>
    <th></th>
    <th>ar</th>
    <th>da</th>
    <th>de</th>
    <th>de-st</th>
    <th>id</th>
    <th>it</th>
    <th>kk</th>
    <th>nl</th>
    <th>sr</th>
    <th>tr</th>
    <th>zh</th>
    <th>Avg</th>
  </tr>
  <tr>
    <th colspan="12">Translate-Train</th>
  </tr>
  <tr>
    <td>WS-TOK</td>
    <td>86.3</td><td>81.3</td><td>86.7</td><td>58.8</td><td>85.0</td><td>88.1</td><td>69.6</td><td>91.3</td><td>86.2</td><td>86.5</td><td>-</td><td>82.0</td>
  </tr>
  <tr>
    <td>LS-TOK</td>
    <td>86.4</td><td>81.4</td><td>87.8</td><td>57.4</td><td>85.8</td><td>87.3</td><td>69.7</td><td>90.8</td><td>83.8</td><td>86.3</td><td>86.2*</td><td>81.6</td>
  </tr>
  <tr>
    <th colspan="12">Translate-Test</th>
  </tr>
  <tr>
    <td>WS-TOK</td>
    <td>73.6</td><td>78.1</td><td>82.7</td><td>57.3</td><td>73.5</td><td>83.4</td><td>61.7</td><td>86.4</td><td>75.9</td><td>75.1</td><td>75.4</td><td>74.8</td>
  </tr>
  <tr>
    <td>LS-TOK</td>
    <td>79.1</td><td>81.3</td><td>89.7</td><td>62.7</td><td>78.7</td><td>89.6</td><td>69.9</td><td>92.9</td><td>80.4</td><td>81.7</td><td>82.7</td><td>80.8</td>
  </tr>
</table>

Table 13: Results for translation-based XLT evaluated on the xSID validation data utilizing different pre-tokenization strategies. We use XLM-R. Results marked with * are excluded from the average.
L  Detailed Results: Main Results and Further Findings

<table>
  <tr>
    <th></th>
    <th colspan="14">bam ewe fon hau ibo kin lug lao mos nya sna swa tsn twi wol xho yor zul Avg</th>
  </tr>
  <tr>
    <th>ZS</th>
    <td>43.4</td><td>72.8</td><td>61.0</td><td>73.5</td><td>49.9</td><td>46.3</td><td>64.9</td><td>55.0</td><td>56.1</td><td>51.1</td><td>34.4</td><td>88.1</td><td>51.5</td><td>49.5</td><td>56.2</td><td>22.2</td><td>35.1</td><td>41.5</td><td>52.9</td>
  </tr>
  <tr>
    <th colspan="20">Translate-Train</th>
  </tr>
  <tr>
    <td>AccAlign</td><td>X</td><td>WS-TOK</td><td>NLLB</td><td>49.2</td><td>74.1</td><td>72.1</td><td>73.1</td><td>72.2</td><td>58.6</td><td>76.4</td><td>63.5</td><td>58.2</td><td>66.2</td><td>70.9</td><td>83.2</td><td>76.5</td><td>64.1</td><td>63.9</td><td>69.6</td><td>40.2</td><td>75.6</td><td>67.1</td>
  </tr>
  <tr>
    <td>AccAlign<sup>†</sup></td><td>X</td><td>WS-TOK</td><td>NLLB</td><td>53.3</td><td>74.0</td><td>71.3</td><td>73.6</td><td>71.0</td><td>58.8</td><td>75.5</td><td>64.2</td><td>55.6</td><td>67.6</td><td>68.7</td><td>83.6</td><td>76.1</td><td>64.5</td><td>61.9</td><td>69.4</td><td>39.3</td><td>72.6</td><td>66.7</td>
  </tr>
  <tr>
    <td>Awesome<sup>†</sup></td><td>X</td><td>WS-TOK</td><td>NLLB</td><td>51.3</td><td>73.8</td><td>65.6</td><td>73.6</td><td>70.0</td><td>56.7</td><td>74.4</td><td>64.6</td><td>50.8</td><td>67.3</td><td>68.4</td><td>82.2</td><td>75.3</td><td>62.4</td><td>58.9</td><td>61.0</td><td>38.4</td><td>64.7</td><td>64.4</td>
  </tr>
  <tr>
    <td>Easy</td><td>X</td><td>-</td><td>NLLB</td><td>54.2</td><td>75.4</td><td>71.1</td><td>73.0</td><td>64.6</td><td>66.3</td><td>77.5</td><td>63.8</td><td>51.3</td><td>68.3</td><td>57.2</td><td>84.1</td><td>74.7</td><td>63.7</td><td>63.3</td><td>71.3</td><td>37.0</td><td>70.6</td><td>66.0</td>
  </tr>
  <tr>
    <td>Codec</td><td>X</td><td>-</td><td>NLLB</td><td>51.2</td><td>74.1</td><td>68.9</td><td>73.4</td><td>65.5</td><td>64.7</td><td>75.4</td><td>64.7</td><td>53.9</td><td>68.3</td><td>70.9</td><td>84.2</td><td>73.5</td><td>65.2</td><td>65.6</td><td>70.2</td><td>39.4</td><td>75.3</td><td>66.9</td>
  </tr>
  <tr>
    <th colspan="20">Translate-Test</th>
  </tr>
  <tr>
    <td>AccAlign</td><td>X</td><td>LS-TOK</td><td>NLLB</td><td>54.7</td><td>79.1</td><td>72.4</td><td>74.0</td><td>73.9</td><td>70.4</td><td>83.8</td><td>73.3</td><td>52.7</td><td>78.6</td><td>81.2</td><td>83.1</td><td>79.2</td><td>70.8</td><td>66.1</td><td>72.8</td><td>57.8</td><td>78.5</td><td>72.3</td>
  </tr>
  <tr>
    <td>AccAlign<sup>†</sup></td><td>X</td><td>LS-TOK</td><td>NLLB</td><td>54.1</td><td>76.3</td><td>69.1</td><td>73.4</td><td>72.4</td><td>69.6</td><td>82.7</td><td>71.4</td><td>48.2</td><td>77.6</td><td>80.0</td><td>81.7</td><td>79.4</td><td>70.5</td><td>62.8</td><td>71.1</td><td>49.4</td><td>77.0</td><td>70.4</td>
  </tr>
  <tr>
    <td>Awesome<sup>†</sup></td><td>X</td><td>LS-TOK</td><td>NLLB</td><td>46.3</td><td>72.4</td><td>58.6</td><td>69.4</td><td>75.3</td><td>65.0</td><td>81.6</td><td>72.1</td><td>42.4</td><td>78.4</td><td>66.3</td><td>80.0</td><td>78.3</td><td>68.9</td><td>53.4</td><td>52.1</td><td>47.7</td><td>64.3</td><td>65.1</td>
  </tr>
  <tr>
    <td>AccAlign</td><td>D</td><td>LS-TOK</td><td>NLLB</td><td>54.3</td><td>79.2</td><td>73.3</td><td>74.5</td><td>75.1</td><td>71.2</td><td>84.0</td><td>75.0</td><td>52.5</td><td>79.1</td><td>81.6</td><td>83.7</td><td>79.3</td><td>70.2</td><td>66.3</td><td>72.9</td><td>57.6</td><td>78.2</td><td>72.7</td>
  </tr>
  <tr>
    <td>AccAlign</td><td>X</td><td>WS-TOK</td><td>NLLB</td><td>44.3</td><td>75.8</td><td>67.3</td><td>74.3</td><td>72.9</td><td>60.2</td><td>80.5</td><td>72.6</td><td>40.4</td><td>68.2</td><td>68.1</td><td>82.9</td><td>76.3</td><td>67.7</td><td>58.2</td><td>61.7</td><td>49.5</td><td>79.5</td><td>69.5</td>
  </tr>
  <tr>
    <td>AccAlign</td><td>D</td><td>WS-TOK</td><td>NLLB</td><td>54.3</td><td>69.7</td><td>56.2</td><td>63.8</td><td>59.9</td><td>58.9</td><td>72.6</td><td>61.6</td><td>40.4</td><td>68.2</td><td>61.4</td><td>68.5</td><td>64.5</td><td>61.3</td><td>55.8</td><td>61.7</td><td>46.2</td><td>61.7</td><td>59.8</td>
  </tr>
  <tr>
    <td>AccAlign</td><td>X</td><td>LS-TOK</td><td>GT</td><td>60.9</td><td>79.3</td><td>73.4</td><td>78.0</td><td>71.7</td><td>-</td><td>-</td><td>-</td><td>-</td><td>83.4</td><td>85.2</td><td>-</td><td>71.3</td><td>-</td><td>75.0</td><td>63.7</td><td>78.2</td><td>-</td><td>-</td>
  </tr>
  <tr>
    <td>AccAlign</td><td>D</td><td>LS-TOK</td><td>GT</td><td>60.2</td><td>79.4</td><td>-</td><td>74.1</td><td>79.1</td><td>72.3</td><td>-</td><td>-</td><td>-</td><td>-</td><td>83.9</td><td>85.9</td><td>-</td><td>72.9</td><td>-</td><td>75.1</td><td>62.2</td><td>78.4</td><td>-</td><td>-</td>
  </tr>
  <tr>
    <td>AccAlign</td><td>ShL</td><td>LS-TOK</td><td>NLLB</td><td>50.0</td><td>66.9</td><td>62.6</td><td>61.9</td><td>59.1</td><td>58.7</td><td>67.1</td><td>61.6</td><td>43.5</td><td>70.7</td><td>64.4</td><td>71.8</td><td>70.0</td><td>59.4</td><td>57.3</td><td>59.4</td><td>44.9</td><td>60.8</td><td>60.6</td>
  </tr>
  <tr>
    <td>AccAlign</td><td>L</td><td>LS-TOK</td><td>NLLB</td><td>46.2</td><td>68.1</td><td>61.0</td><td>62.1</td><td>58.0</td><td>56.9</td><td>69.0</td><td>60.9</td><td>43.0</td><td>69.0</td><td>67.8</td><td>72.5</td><td>66.7</td><td>56.4</td><td>56.9</td><td>59.3</td><td>45.5</td><td>65.5</td><td>60.3</td>
  </tr>
  <tr>
    <td>Codec</td><td>X</td><td>-</td><td>NLLB</td><td>54.5</td><td>78.8</td><td>67.4</td><td>72.9</td><td>72.8</td><td>77.6</td><td>83.6</td><td>72.8</td><td>49.4</td><td>78.1</td><td>79.3</td><td>82.2</td><td>79.2</td><td>72.5</td><td>67.3</td><td>72.5</td><td>58.4</td><td>77.1</td><td>72.0</td>
  </tr>
  <tr>
    <td>Codec</td><td>D</td><td>-</td><td>NLLB</td><td>54.3</td><td>79.1</td><td>68.0</td><td>73.3</td><td>73.9</td><td>78.2</td><td>83.5</td><td>74.2</td><td>48.8</td><td>79.0</td><td>79.8</td><td>82.9</td><td>79.3</td><td>73.1</td><td>67.8</td><td>72.6</td><td>58.0</td><td>77.0</td><td>72.4</td>
  </tr>
  <tr>
    <th colspan="20">Ensembling-Translate-Train</th>
  </tr>
  <tr>
    <td>AccAlign + AccAlign</td><td>X + X</td><td>WS + WS-TOK</td><td>NLLB</td><td>57.0</td><td>79.1</td><td>74.3</td><td>72.6</td><td>77.6</td><td>63.5</td><td>81.8</td><td>69.8</td><td>59.4</td><td>74.9</td><td>75.6</td><td>83.8</td><td>78.4</td><td>66.3</td><td>67.6</td><td>72.0</td><td>52.6</td><td>78.7</td><td>71.4</td>
  </tr>
  <tr>
    <td>AccAlign + AccAlign</td><td>X + X</td><td>WS + LS-TOK</td><td>NLLB</td><td>57.3</td><td>78.5</td><td>75.7</td><td>72.8</td><td>79.0</td><td>64.1</td><td>82.5</td><td>70.3</td><td>60.2</td><td>75.3</td><td>77.2</td><td>84.3</td><td>78.9</td><td>66.7</td><td>68.0</td><td>72.4</td><td>53.5</td><td>78.8</td><td>72.0</td>
  </tr>
  <tr>
    <td>AccAlign + AccAlign</td><td>X + D</td><td>WS + WS-TOK</td><td>NLLB</td><td>56.9</td><td>79.5</td><td>75.2</td><td>73.3</td><td>79.0</td><td>64.2</td><td>81.7</td><td>71.5</td><td>59.3</td><td>75.6</td><td>76.4</td><td>83.9</td><td>79.3</td><td>67.5</td><td>68.3</td><td>72.8</td><td>53.0</td><td>79.1</td><td>72.0</td>
  </tr>
  <tr>
    <td>AccAlign + AccAlign</td><td>X + D</td><td>WS + LS-TOK</td><td>NLLB</td><td>57.0</td><td>79.2</td><td>76.4</td><td>73.5</td><td>80.6</td><td>64.7</td><td>82.7</td><td>71.9</td><td>60.2</td><td>76.0</td><td>77.5</td><td>84.2</td><td>79.9</td><td>68.3</td><td>68.8</td><td>72.9</td><td>53.9</td><td>79.1</td><td>72.6</td>
  </tr>
  <tr>
    <td>AccAlign + AccAlign</td><td>X + D</td><td>WS + LS-TOK</td><td>GT</td><td>61.0</td><td>79.0</td><td>-</td><td>73.6</td><td>81.1</td><td>64.9</td><td>-</td><td>-</td><td>-</td><td>-</td><td>78.5</td><td>85.0</td><td>-</td><td>67.8</td><td>-</td><td>74.0</td><td>58.2</td><td>80.0</td><td>-</td>
  </tr>
  <tr>
    <td>AccAlign + AccAlign</td><td>X + ShL</td><td>WS + LS-TOK</td><td>NLLB</td><td>55.6</td><td>76.5</td><td>73.4</td><td>73.0</td><td>75.7</td><td>60.5</td><td>78.9</td><td>66.4</td><td>58.6</td><td>78.1</td><td>73.1</td><td>83.3</td><td>78.1</td><td>64.3</td><td>66.5</td><td>70.5</td><td>46.3</td><td>76.3</td><td>69.3</td>
  </tr>
  <tr>
    <td>AccAlign + AccAlign</td><td>X + L</td><td>WS + LS-TOK</td><td>NLLB</td><td>53.9</td><td>77.2</td><td>74.2</td><td>72.9</td><td>74.2</td><td>60.5</td><td>80.0</td><td>67.1</td><td>58.2</td><td>72.1</td><td>74.1</td><td>83.1</td><td>78.0</td><td>63.8</td><td>67.2</td><td>70.5</td><td>47.2</td><td>77.5</td><td>69.5</td>
  </tr>
  <tr>
    <td>Easy + AccAlign</td><td>X + D</td><td>+ LS-TOK</td><td>NLLB</td><td>58.0</td><td>79.2</td><td>74.0</td><td>73.3</td><td>76.4</td><td>64.5</td><td>83.2</td><td>71.1</td><td>56.3</td><td>77.8</td><td>72.6</td><td>85.2</td><td>78.7</td><td>68.6</td><td>68.3</td><td>73.8</td><td>53.6</td><td>76.3</td><td>71.7</td>
  </tr>
  <tr>
    <td>Codec + AccAlign</td><td>X + D</td><td>+ LS-TOK</td><td>NLLB</td><td>55.4</td><td>79.2</td><td>74.9</td><td>73.5</td><td>77.7</td><td>63.4</td><td>82.4</td><td>72.2</td><td>57.9</td><td>77.4</td><td>77.6</td><td>85.0</td><td>77.9</td><td>69.3</td><td>69.4</td><td>74.0</td><td>54.3</td><td>79.1</td><td>72.3</td>
  </tr>
</table>

Table 14: Main results for translation-based XLT evaluated on Masakha using different WAs, pre-tokenizations, and MT models. We use XLM-R (X), DeBERTa (D), LLM2Vec Sheared-Llama 1.3B (ShL), and LLM2Vec LLama 3 8B (L).
<table>
  <tr>
    <th></th>
    <th>ar</th>
    <th>da</th>
    <th>de</th>
    <th>de-st</th>
    <th>id</th>
    <th>it</th>
    <th>kk</th>
    <th>nl</th>
    <th>sr</th>
    <th>tr</th>
    <th>zh</th>
    <th>Avg</th>
  </tr>
  <tr>
    <td>ZS</td>
    <td>71.5</td>
    <td>85.6</td>
    <td>80.8</td>
    <td>43.9</td>
    <td>86.8</td>
    <td>88.2</td>
    <td>80.8</td>
    <td>88.8</td>
    <td>79.0</td>
    <td>81.5</td>
    <td>57.4</td>
    <td>76.8</td>
  </tr>
</table>

Translate-Train

<table>
  <tr>
    <th></th>
    <th></th>
    <th></th>
    <th></th>
    <th></th>
    <th></th>
    <th></th>
    <th></th>
    <th></th>
    <th></th>
    <th></th>
    <th></th>
    <th></th>
  </tr>
  <tr>
    <td>AccAlign</td>
    <td>X</td>
    <td>WS-TOK</td>
    <td>NLLB</td>
    <td>82.6</td>
    <td>76.0</td>
    <td>86.1</td>
    <td>62.2</td>
    <td>87.4</td>
    <td>88.1</td>
    <td>86.0</td>
    <td>85.5</td>
    <td>85.0</td>
    <td>85.3</td>
    <td>85.1</td>
    <td>82.7</td>
  </tr>
  <tr>
    <td>AccAlign†</td>
    <td>X</td>
    <td>WS-TOK</td>
    <td>NLLB</td>
    <td>81.8</td>
    <td>76.4</td>
    <td>87.7</td>
    <td>63.8</td>
    <td>82.9</td>
    <td>87.8</td>
    <td>85.7</td>
    <td>85.4</td>
    <td>86.6</td>
    <td>87.2</td>
    <td>86.7</td>
    <td>82.9</td>
  </tr>
  <tr>
    <td>Awesome†</td>
    <td>X</td>
    <td>WS-TOK</td>
    <td>NLLB</td>
    <td>79.1</td>
    <td>77.1</td>
    <td>85.6</td>
    <td>61.3</td>
    <td>82.7</td>
    <td>87.3</td>
    <td>74.3</td>
    <td>85.8</td>
    <td>84.5</td>
    <td>77.7</td>
    <td>82.4</td>
    <td>79.8</td>
  </tr>
  <tr>
    <td>Easy</td>
    <td>X</td>
    <td>-</td>
    <td>NLLB</td>
    <td>83.0</td>
    <td>84.0</td>
    <td>89.4</td>
    <td>62.2</td>
    <td>86.3</td>
    <td>87.5</td>
    <td>89.2</td>
    <td>88.3</td>
    <td>81.4</td>
    <td>86.3</td>
    <td>80.5</td>
    <td>83.4</td>
  </tr>
  <tr>
    <td>Codec</td>
    <td>X</td>
    <td>-</td>
    <td>NLLB</td>
    <td>81.9</td>
    <td>84.6</td>
    <td>88.7</td>
    <td>62.5</td>
    <td>89.8</td>
    <td>88.5</td>
    <td>85.1</td>
    <td>89.9</td>
    <td>82.7</td>
    <td>81.2</td>
    <td>84.4</td>
    <td>83.6</td>
  </tr>
</table>

Translate-Test

<table>
  <tr>
    <th></th>
    <th></th>
    <th></th>
    <th></th>
    <th></th>
    <th></th>
    <th></th>
    <th></th>
    <th></th>
    <th></th>
    <th></th>
    <th></th>
    <th></th>
    <th></th>
    <th></th>
    <th></th>
  </tr>
  <tr>
    <td>AccAlign</td>
    <td>X</td>
    <td>LS-TOK</td>
    <td>NLLB</td>
    <td>78.8</td>
    <td>76.0</td>
    <td>86.3</td>
    <td>60.9</td>
    <td>78.8</td>
    <td>88.1</td>
    <td>82.5</td>
    <td>87.5</td>
    <td>79.8</td>
    <td>81.3</td>
    <td>82.3</td>
    <td>80.2</td>
  </tr>
  <tr>
    <td>AccAlign†</td>
    <td>X</td>
    <td>LS-TOK</td>
    <td>NLLB</td>
    <td>77.8</td>
    <td>75.4</td>
    <td>84.8</td>
    <td>59.6</td>
    <td>79.3</td>
    <td>85.7</td>
    <td>82.1</td>
    <td>86.8</td>
    <td>80.0</td>
    <td>82.0</td>
    <td>82.6</td>
    <td>79.6</td>
  </tr>
  <tr>
    <td>Awesome†</td>
    <td>X</td>
    <td>LS-TOK</td>
    <td>NLLB</td>
    <td>73.8</td>
    <td>74.9</td>
    <td>84.8</td>
    <td>59.2</td>
    <td>81.0</td>
    <td>84.4</td>
    <td>63.0</td>
    <td>87.2</td>
    <td>78.6</td>
    <td>76.8</td>
    <td>74.8</td>
    <td>78.6</td>
  </tr>
  <tr>
    <td>AccAlign</td>
    <td>D</td>
    <td>LS-TOK</td>
    <td>NLLB</td>
    <td>73.8</td>
    <td>74.9</td>
    <td>84.8</td>
    <td>59.1</td>
    <td>81.0</td>
    <td>84.4</td>
    <td>63.0</td>
    <td>87.2</td>
    <td>78.6</td>
    <td>76.8</td>
    <td>74.8</td>
    <td>78.6</td>
  </tr>
  <tr>
    <td>AccAlign</td>
    <td>X</td>
    <td>WS-TOK</td>
    <td>NLLB</td>
    <td>72.9</td>
    <td>70.6</td>
    <td>79.1</td>
    <td>55.7</td>
    <td>72.2</td>
    <td>81.2</td>
    <td>74.2</td>
    <td>82.0</td>
    <td>73.8</td>
    <td>72.9</td>
    <td>72.6</td>
    <td>73.4</td>
  </tr>
  <tr>
    <td>AccAlign</td>
    <td>D</td>
    <td>WS-TOK</td>
    <td>NLLB</td>
    <td>73.4</td>
    <td>70.2</td>
    <td>77.9</td>
    <td>53.6</td>
    <td>73.3</td>
    <td>82.2</td>
    <td>74.2</td>
    <td>80.7</td>
    <td>73.8</td>
    <td>73.4</td>
    <td>72.5</td>
    <td>73.2</td>
  </tr>
  <tr>
    <td>AccAlign</td>
    <td>X</td>
    <td>LS-TOK</td>
    <td>GT</td>
    <td>80.5</td>
    <td>76.5</td>
    <td>86.4</td>
    <td>61.8</td>
    <td>78.6</td>
    <td>88.2</td>
    <td>83.8</td>
    <td>87.0</td>
    <td>82.2</td>
    <td>81.6</td>
    <td>86.2</td>
    <td>81.2</td>
  </tr>
  <tr>
    <td>AccAlign</td>
    <td>D</td>
    <td>LS-TOK</td>
    <td>GT</td>
    <td>80.5</td>
    <td>76.5</td>
    <td>85.6</td>
    <td>59.3</td>
    <td>79.6</td>
    <td>89.3</td>
    <td>84.0</td>
    <td>87.1</td>
    <td>83.4</td>
    <td>82.4</td>
    <td>85.7</td>
    <td>81.2</td>
  </tr>
  <tr>
    <td>AccAlign</td>
    <td>ShL</td>
    <td>LS-TOK</td>
    <td>NLLB</td>
    <td>68.4</td>
    <td>68.3</td>
    <td>76.8</td>
    <td>51.6</td>
    <td>68.6</td>
    <td>77.7</td>
    <td>72.2</td>
    <td>78.1</td>
    <td>70.7</td>
    <td>72.2</td>
    <td>73.3</td>
    <td>70.7</td>
  </tr>
  <tr>
    <td>AccAlign</td>
    <td>L</td>
    <td>LS-TOK</td>
    <td>NLLB</td>
    <td>68.6</td>
    <td>69.2</td>
    <td>76.1</td>
    <td>50.1</td>
    <td>69.9</td>
    <td>76.9</td>
    <td>70.4</td>
    <td>77.8</td>
    <td>70.8</td>
    <td>71.1</td>
    <td>72.8</td>
    <td>70.3</td>
  </tr>
  <tr>
    <td>Codec</td>
    <td>X</td>
    <td>LS-TOK</td>
    <td>NLLB</td>
    <td>79.0</td>
    <td>81.9</td>
    <td>86.1</td>
    <td>60.4</td>
    <td>84.8</td>
    <td>88.4</td>
    <td>83.0</td>
    <td>86.5</td>
    <td>72.4</td>
    <td>83.6</td>
    <td>67.0</td>
    <td>79.4</td>
  </tr>
  <tr>
    <td>Codec</td>
    <td>D</td>
    <td>LS-TOK</td>
    <td>NLLB</td>
    <td>79.9</td>
    <td>81.8</td>
    <td>85.5</td>
    <td>58.8</td>
    <td>85.8</td>
    <td>89.0</td>
    <td>83.2</td>
    <td>86.0</td>
    <td>72.9</td>
    <td>84.2</td>
    <td>67.5</td>
    <td>79.5</td>
  </tr>
</table>

Ensembling-Translate-Train

<table>
  <tr>
    <th></th>
    <th></th>
    <th></th>
    <th></th>
    <th></th>
    <th></th>
    <th></th>
    <th></th>
    <th></th>
    <th></th>
    <th></th>
    <th></th>
    <th></th>
    <th></th>
    <th></th>
    <th></th>
  </tr>
  <tr>
    <td>AccAlign + AccAlign</td>
    <td>X + X</td>
    <td>WS- + WS-TOK</td>
    <td>NLLB</td>
    <td>80.9</td>
    <td>75.1</td>
    <td>85.9</td>
    <td>63.1</td>
    <td>88.1</td>
    <td>89.2</td>
    <td>86.3</td>
    <td>82.7</td>
    <td>81.5</td>
    <td>80.4</td>
    <td>81.3</td>
    <td></td>
  </tr>
  <tr>
    <td>AccAlign + AccAlign</td>
    <td>X + X</td>
    <td>WS- + LS-TOK</td>
    <td>NLLB</td>
    <td>82.0</td>
    <td>76.3</td>
    <td>88.2</td>
    <td>64.5</td>
    <td>88.7</td>
    <td>89.6</td>
    <td>86.5</td>
    <td>86.4</td>
    <td>82.9</td>
    <td>85.0</td>
    <td>83.2</td>
    <td></td>
  </tr>
  <tr>
    <td>AccAlign + AccAlign</td>
    <td>X + D</td>
    <td>WS- + WS-TOK</td>
    <td>NLLB</td>
    <td>81.2</td>
    <td>75.0</td>
    <td>85.8</td>
    <td>61.7</td>
    <td>88.2</td>
    <td>89.9</td>
    <td>81.1</td>
    <td>86.6</td>
    <td>82.6</td>
    <td>81.7</td>
    <td>79.6</td>
    <td>81.2</td>
  </tr>
  <tr>
    <td>AccAlign + AccAlign</td>
    <td>X + D</td>
    <td>WS- + LS-TOK</td>
    <td>NLLB</td>
    <td>82.4</td>
    <td>76.1</td>
    <td>88.2</td>
    <td>64.2</td>
    <td>89.0</td>
    <td>90.0</td>
    <td>86.6</td>
    <td>87.2</td>
    <td>83.1</td>
    <td>85.3</td>
    <td>84.8</td>
    <td>83.4</td>
  </tr>
  <tr>
    <td>AccAlign + AccAlign</td>
    <td>X + D</td>
    <td>WS- + LS-TOK</td>
    <td>GT</td>
    <td>82.4</td>
    <td>75.8</td>
    <td>87.1</td>
    <td>64.3</td>
    <td>87.7</td>
    <td>89.8</td>
    <td>86.5</td>
    <td>86.3</td>
    <td>84.4</td>
    <td>84.2</td>
    <td>86.4</td>
    <td>83.2</td>
  </tr>
  <tr>
    <td>AccAlign + AccAlign</td>
    <td>X + ShL</td>
    <td>WS- + LS-TOK</td>
    <td>NLLB</td>
    <td>81.2</td>
    <td>76.2</td>
    <td>87.4</td>
    <td>63.6</td>
    <td>87.7</td>
    <td>89.6</td>
    <td>86.0</td>
    <td>86.1</td>
    <td>83.7</td>
    <td>84.7</td>
    <td>84.5</td>
    <td>82.8</td>
  </tr>
  <tr>
    <td>AccAlign + AccAlign</td>
    <td>X + L</td>
    <td>WS- + LS-TOK</td>
    <td>NLLB</td>
    <td>79.7</td>
    <td>76.4</td>
    <td>87.3</td>
    <td>61.8</td>
    <td>86.1</td>
    <td>88.4</td>
    <td>85.4</td>
    <td>86.1</td>
    <td>82.9</td>
    <td>83.3</td>
    <td>84.0</td>
    <td>81.9</td>
  </tr>
  <tr>
    <td>Easy + AccAlign</td>
    <td>X + D</td>
    <td>+ - LS-TOK</td>
    <td>NLLB</td>
    <td>81.1</td>
    <td>81.3</td>
    <td>89.7</td>
    <td>64.4</td>
    <td>89.0</td>
    <td>90.6</td>
    <td>85.7</td>
    <td>90.4</td>
    <td>82.7</td>
    <td>84.2</td>
    <td>84.5</td>
    <td>83.8</td>
  </tr>
  <tr>
    <td>Codec + AccAlign</td>
    <td>X + D</td>
    <td>+ - LS-TOK</td>
    <td>NLLB</td>
    <td>82.5</td>
    <td>75.3</td>
    <td>89.1</td>
    <td>62.7</td>
    <td>87.6</td>
    <td>88.7</td>
    <td>87.1</td>
    <td>89.2</td>
    <td>81.8</td>
    <td>85.3</td>
    <td>81.3</td>
    <td>82.8</td>
  </tr>
</table>

Table 15: Main results for translation-based XLT evaluated on xSID using different WAs, pre-tokenizations, and MT models. We use XLM-R (X), DeBERTa (D), LLM2Vec Sheared-Llama 1.3B (ShL), and LLM2Vec LLama 3 8B (L).

<table>
  <tr>
    <th></th>
    <th>hau</th>
    <th>ibo</th>
    <th>nya</th>
    <th>sna</th>
    <th>swa</th>
    <th>xho</th>
    <th>yor</th>
    <th>zul</th>
    <th>Avg</th>
  </tr>
  <tr>
    <td>Easy</td>
    <td>X</td>
    <td>73.0</td>
    <td>64.6</td>
    <td>68.3</td>
    <td>57.2</td>
    <td>84.1</td>
    <td>71.3</td>
    <td>37.0</td>
    <td>70.6</td>
    <td>65.8</td>
  </tr>
  <tr>
    <td>Easy</td>
    <td>mD</td>
    <td>73.1</td>
    <td>66.9</td>
    <td>74.5</td>
    <td>48.9</td>
    <td>84.4</td>
    <td>69.3</td>
    <td>35.6</td>
    <td>70.6</td>
    <td>65.4</td>
  </tr>
  <tr>
    <td>CLaP</td>
    <td>X</td>
    <td>68.2</td>
    <td>50.5</td>
    <td>63.5</td>
    <td>66.5</td>
    <td>77.0</td>
    <td>63.1</td>
    <td>33.6</td>
    <td>63.4</td>
    <td>60.7</td>
  </tr>
  <tr>
    <td>CLaP</td>
    <td>mD</td>
    <td>65.4</td>
    <td>51.1</td>
    <td>72.8</td>
    <td>72.0</td>
    <td>77.0</td>
    <td>63.0</td>
    <td>33.5</td>
    <td>62.7</td>
    <td>62.2</td>
  </tr>
  <tr>
    <td>T-Proj</td>
    <td>X</td>
    <td>71.9</td>
    <td>64.2</td>
    <td>66.0</td>
    <td>68.7</td>
    <td>83.7</td>
    <td>68.4</td>
    <td>37.8</td>
    <td>70.5</td>
    <td>66.4</td>
  </tr>
  <tr>
    <td>T-Proj</td>
    <td>mD</td>
    <td>72.2</td>
    <td>67.7</td>
    <td>75.7</td>
    <td>73.7</td>
    <td>82.9</td>
    <td>68.8</td>
    <td>40.6</td>
    <td>70.2</td>
    <td>69.0</td>
  </tr>
  <tr>
    <td>Codec</td>
    <td>X</td>
    <td>73.4</td>
    <td>65.5</td>
    <td>68.3</td>
    <td>70.9</td>
    <td>84.2</td>
    <td>70.2</td>
    <td>39.4</td>
    <td>75.3</td>
    <td>68.4</td>
  </tr>
  <tr>
    <td>Codec</td>
    <td>mD</td>
    <td>72.5</td>
    <td>67.1</td>
    <td>74.9</td>
    <td>73.2</td>
    <td>82.8</td>
    <td>69.2</td>
    <td>41.1</td>
    <td>72.4</td>
    <td>69.2</td>
  </tr>
  <tr>
    <td>WA</td>
    <td>X</td>
    <td>73.1</td>
    <td>72.2</td>
    <td>66.2</td>
    <td>70.9</td>
    <td>83.2</td>
    <td>69.6</td>
    <td>40.2</td>
    <td>75.6</td>
    <td>68.9</td>
  </tr>
  <tr>
    <td>WA</td>
    <td>mD</td>
    <td>72.7</td>
    <td>68.7</td>
    <td>75.6</td>
    <td>73.0</td>
    <td>81.8</td>
    <td>69.4</td>
    <td>40.6</td>
    <td>71.7</td>
    <td>69.2</td>
  </tr>
</table>

Table 16: Results for T-Train on Masakha using different projection methods and downstream models—XLM-R (X) and mDeBERTa (mD). For WA, we use the same setting as for our main results. We subset the languages to those seen in the pretraining of mT5 which is the backbone model of T-Proj (García-Ferrero et al., 2023).

Reviewer paper summaries: This paper investigates the role of word aligners (WA) in translation-based XLT for token classification tasks, experimenting on 3 benchmarks across 35 languages, and shows that WA-based method outperforms marker-based method. They further propose to ensemble the predictions from translation-train&test with a WA-base method.

This article proposes a method for artificially creating a corpus in a new language. This involves translating or aligning a corpus annotated in a source language into a target language, while projecting the labels through translation alignments. The authors then evaluate the impact of the alignment method on the performance obtained.
Summary of strengths: - The experimental setup is comprehensive, and the evaluation dataset has a wide range of applications. The combination of charts and tables clearly presents the results.  
- The paper introduces a word aligner-based method, which in many settings matches or even surpasses the performance of the marker-based method.  
- Section 4.3 provides further discussion on issues such as using LLM as a vector and translation quality.

- intringuing results
Summary of weaknesses: - The paper lacks a Related Work section. Since the marker-based method is considered a primary competitor, there should be a discussion in the related work section describing this method and explaining the differences between the two approaches to highlight the novelty of this work.  
- Some parts of the writing appear structurally repetitive. For example, between lines 184 and 192, the phrase "we then" appears three times, along with one occurrence of "we next". It may be better to present this process as a list, algorithm, or diagram to improve readability in the methodology section.  
- Regarding Figure 3, I am curious about the intrinsic ability of LLMs to handle this type of problem. Since the performance of LLM as a vector is even worse than that of mLMs, could this be due to an inappropriate structural setup for LLM as a vector?

The article presents several difficulties of comprehension. Firstly, its aim is unclear: is it to introduce a new projection method (in which case it would be essential to justify how this method is innovative compared with the existing one) or to analyze the impact of alignments on a projection method (in which case the use of projection methods recognized as state-of-the-art would be essential to make the conclusions interesting)? This ambiguity is perceptible right from the introduction, which remains vague and too generalist. It does not clearly situate the problem in a wider context, which is particularly important for annotation transfer, a problem that the authors themselves recognize as potentially obsolete in the era of multilingual models. It would therefore be crucial to justify in which situations this type of approach remains relevant.

The description of the projection method is also difficult to follow and would have benefited from more detail. For example, the main advantage seems to be the transfer of “structured” labels (such as those encoded with the BIO format), as opposed to methods that are limited to the transfer of labels in simple or ambiguous cases (e.g. [1]). This distinction is not made clear in the text. What's more, the method itself seems unnecessarily complex. A simpler reformulation could be based on sentence alignment heuristics, which are at the heart of phrase-based translation systems. Indeed, it seems that an approach consisting of considering only the first and last words translated would suffice to determine which segments to label, without requiring more complex processing.

[1] Frustratingly Easy Cross-Lingual Transfer for Transition-Based Dependency Parsing, NAACL'19
Comments, suggestions and typos: Please see Weaknesses above.

see above
Overall assessment scores: 2.5

2