Improving Semantic Proximity in Information Retrieval through Cross-Lingual Alignment
Authors: Seongtae Hong
Youngjoon Jang
Jungseob Lee
Hyeonseok Moon
Heuiseok Lim
Venue: ICLR 2026 Poster (2026)
Keywords: Cross-Lingual Alignment
Information Retrieval
Multilingual Embedding
Cross-Lingual Information Retrieval
Primary area: unsupervised, self-supervised, semi-supervised, and supervised representation learning
TL;DR: This paper identifies multilingual embedding gaps in cross-lingual retrieval, proposes scenario and Max@R metric, and introduces a training strategy combining JSD and InfoNCE loss, significantly improving cross-lingual alignment with minimal data.

Abstract: With the increasing accessibility and utilization of multilingual documents, Cross-Lingual Information Retrieval (CLIR) has emerged as an important research area. Conventionally, CLIR tasks have been conducted under settings where the language of documents differs from that of queries, and typically, the documents are composed in a single coherent language. In this paper, we highlight that in such a setting, the cross-lingual alignment capability may not be evaluated adequately. Specifically, we observe that, in a document pool where English documents coexist with another language, most multilingual retrievers tend to prioritize unrelated English documents over the related document written in the same language as the query. To rigorously analyze and quantify this phenomenon, we introduce various scenarios and metrics designed to evaluate the cross-lingual alignment performance of multilingual retrieval models. Furthermore, to improve cross-lingual performance under these challenging conditions, we propose a novel training strategy aimed at enhancing cross-lingual alignment. Using only a small dataset consisting of 2.8k samples, our method significantly improves the cross-lingual retrieval performance while simultaneously mitigating the English inclination problem. Extensive analyses demonstrate that the proposed method substantially enhances the cross-lingual alignment capabilities of most multilingual embedding models.

Published as a conference paper at ICLR 2026
IMPROVING SEMANTIC PROXIMITY IN INFORMATION RETRIEVAL THROUGH
ALIGNMENT
Seongtae Hong, Youngjoon Jang, Jungseob Lee, Hyeonseok Moon", Heuiseok Lim" Department of Computer Science and Engineering, Korea University (ghdchlwis) 23, dew 1701, omanma glee889, limhseok) @ korea.ac.kr
ABSTRACT
With the increasing accessibility and utilization of multilingual documents, Cross- Lingual Information Retrieval (CLIR) has emerged as an important research area. Conventionally, CLIR tasks have been conducted under settings where the lan- guage of documents differs from that of queries, and typically, the documents are composed a single coherent language. In this paper, we highlight that in such setting, the cross-lingual alignment capability may not be evaluated adequately. Specifically, we observe that, in document pool where English documents coexist with another language, most multilingual retrievers tend to prioritize unrelated English documents over the related document written in the same language as the query. To rigorously analyze and quantify this phenomenon, we introduce various scenarios and metrics designed to evaluate the cross-lingual alignment performance of multilingual retrieval models. Furthermore, to improve cross- booss-liguual perfor- mance under these challenging conditions, we propose a novel training strategy aimed at enhancing cross-lingual alignment. Using only small dataset consist- ing of 2.8k samples, our method significantly improves the cross-lingual retrieval performance while simultaneously mitigating the English inclination problem. Ex- tensive analyses demonstrate that the proposed method substantially enhances the cross-lingual alignment capabilities of most multilingual embedding models.
INTRODUCTION
Information Retrieval (IR) is core technology that accurately identifies and provides relevant information from large-scale document collections based on user queries. With the recent increase in multilingual data and the growing demand for accessing it, the importance of Cross-Lingual
Information Retrieval (CLIR) has become more pronounced (Adeyemi et al. 2024] Guo et al. 2024]
Zhang et al. 22023 Mayfield et al. 2023). Commonly adopted CLIR evaluation settings primarily focus on scenarios where the query language differs from the language of the document pool, which composed of a single language (Lawrie et al., 2023a] [raschher 2003). The objective to measure a set of documents in a language different from that of query, thereby assessing the cross-lingual representation capability when query and document languages are not equal. Similarly, the Multilingual Information Retrieval (MLIR) task involves retrieving and ranking relevant documents from an integrated collection containing three or more languages (Lawric et al. 2024).
retrieval performance
Despite these efforts, we observe that the practical effectiveness of multilingual embedding models in these settings has not been adequately assessed, and significant performance blind spots remain. Our research question is whether multilingual embedding models can consistently maintain retrieval performance for queries given in different languages, in realistic scenarios where documents in two languages coexist, without performance degradation stemming from cross-lingual semantic misalignment language-specifie biases. In fact, our exploratory analysis reveals that when retrieving from a document pool containing both English and another language that matches the query language, many multilingual embedding models exhibit significant cross-lingual semantic misalignment and language bias toward certain languages, severely degrading retrieval performance (Wu & Dredze 2020; Park & Lee 2025 Yang et al. 2024 Elmahdy et al. 2024). For instance, when a query is
Coorreponndng author

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Published as a conference paper at ICLR 2026
written in language A, ideally, both the relevant document written in language A and its semantically equivalent counterpart in English should be ranked at the top of the retrieval results. However, due to prioritization bias toward English-a high-resource language-irreles appear higher the rankings, while the correct documents written in language A are relegated to lower ranks. Furthermore, we observe pronounced language-dependent performance disparity, in which retrieval results vary significantly depending on the language of the query. This phenomenon highlights a critical limitation that is difficult accurately measure or analyze using conventional evaluation settings. Consequently, our findings underscore the necessity of designing a more practical evaluation environment and corresponding metrics, distinct from existing evaluation settings. to rigorously validate balanced cross-lingual semantic alignment.
English documents often
In this paper, we define a scenario where documents in two languages coexist to comprehensively evaluate tross-lingual alignment capabilities in retrieval, and in-
Document Englih]
Decument [Target]
Arcal 18,61 61
Arna
Logit
Logit
troduce a new evaluation metric, Max @ R.
By assessing embedding models within this scenario and using the proposed metric, we provide detailed analysis of both cross- lingual alignment aspects and retrieval per- formance, as well as an in-depth exami- nation of whether there exists an inherent preference for a particular language.
Embedding Dimension
Embedding Dimension
Ours Illustration of distribution-level alignment. Our method shows better alignment than InfoNCE, even when both share the same cosine similarity of 0.99.
InfoNCE
Figure 1:
Furthermore, based on our analysis of this scenario, we discuss methods to effectively mitigate cross-lingual misalignment. In particular, we propose unified training strategy that combines two loss functions to jointly optimize cross- cross-lingual alignment and retrieval performance. Specifically, our strategy integrates -hnsen-Shannon Divergence (JSD) (Lin 1991), which aligns the semantic embedding distributions across languages by adjusting the embedding space, with InfoNCE (van den Oord et al., 2019). which directly enhances the retrieval ability between query and document. By applying proposed method, we demonstrate that even with a relatively small dataset of only 2.8K samples, multilingual embedding models can achieve substantially enhanced cross-lingual alignment and retrieval capabilities. Moreover, find that performance of our method in monolingual settings, including conventional CLIR, can be maintained or even improved relative to the baseline models.
2 PRELIMINARY
In this section, we rigorously define the cross-lingual alignment problem that arises in the evaluation of multilingual embedding models. To accomplish this, introduce the experimental environment of both CLIR and MLIR, followed by detailed explanation of the problem definition.
2.1 CONVENTIONAL SETTINGS
The objective of a typical IR task is to identify the most relevant documents from a collection in re- sponse to given query. This collection of documents is usually represented D {d,, d2, d2..... ). When a query (q) is provided, similarity scores are calculated for the documents within the collection (D), and the most similar documents are retrieved based these scores. CLIR is characterized by the difference between the query language (I.g) and the language of the document collection (La), such that Ly + La. For instance, when query provided in specific language (L1), the goal is to retrieve the most relevant documents written in a different language (L2). MLIR, on the other hand, aims effectively retrieve the documents from collection composed of documents in multiple languages (La {L2, L3. ......) when the query is presented in single specific language (L,- L1).
2.2 PROBLEM DEFINITION
Existing evaluation settings primarily address scenarios where the query and document collection languages differ, or where multiple languages are included within documents (Lawrie et al.) 2023b). However, these approaches limited to assessing model's basic cross-lingual retrieval capabilities
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shallow multilingual retrieval performance, fail to reveal deeper issues such iaaccrate cross- lingual alignment and language bias. Therefore, we consider a multi-reference cross- cross-lingual setup that can comprchensively evaluate both cross lingual alignment and retrieval performance. For instance, in scenario where the document pool contains a mixture of two languages, 11 and T22, an effective model should rank all semantically relevant documents at the top, inrespective of the languages used in queries or documents. In other words, documents related to the query, even if expressed in different languages, should be retrieved with equal importance. Ideally, the model should demonstrate equal performance for queries in each language. To simulate an information retrieval scenario in such an environment, we construct and analyze an experimental setup using a dataset that fully parallel across languages.
multilingual-e5
gte- gte-multilingua
jina- v3---enngddnnd
bge-m3
Query [Engkin)
Query [Target]]
6:13
(a)
5.54
0.59
0.51
9.32
1.20
0.85
AR
V1
231
T14
AR
E5
0.7
Coury (igias)
20
2.7
26-
13:
2.3
16
557.9
21.3
14
1.
200
336.6
20
100
25.6
163.2
112.4
2.8
13.
AR
2H
25
TH
AR
ZH
E5
TH
VI
ARt
2H
TM
V1
AR
ZH
TH
Figure 2: Performance comparison on the XQuAD dataset: (a) CLIR setting, and (b) Multi scenario. We evaluate four multilingual embedding models on five target languages: Arabic (AR), Chinese (ZH), Spanish (ES), Thai (TH), and Vietnamese (VI). Values indicate Max @ R gaps.
3 MULTI-REFERENCE IN CROSS-LINGUAL INFORMATION RETRIEVAL
Insufficient cross-lingual semantic alignment results in the similarity between query and documents in other languages not being properly reflected, causing two relevant documents to fail to be ranked at the top. To quantitatively measure this issue and effectively evaluate performance in this scenario where two languages coexist, we propose new evaluation metric.
Max@R R We evaluate retrieval accuracy for a given query (g) using set of reference documents, denoted as R. = (ri, (2...2..... J'm In our multi-reference cross-lingual scenario, these documents are parallel ground-truths written in different languages. For the given query of retrieved documents as D'(q) = {d', dy d,).. The documents are sorted by retrieval priority based semantic similarity to q. We define the position i of document d' in the list D'(g) as its rank. To address the limitation that existing metries (MAP, MRR, or NDCG@k) are not designed measure when all parallel ground-truths in this multi-reference scenario have been retrieved, propose Max @ R as an essential diagnostic metric. Then, we define Max R as follows:
denote ordered list
Max @ R = max((i) d; R2y))
(1)
By calculating Max @R in this manner, Max @ R represents the highest (i.e., worst) rank which all relevant documents in the reference set are first found in retrieval results. This is, in effect, the "actual cutoff point" one must reach to retrieve all documents in In other words, a lower Max @ R value indicates that all relevant documents can be retrieved within a smaller portion of the top-ranked results, signifying a more efficient and
retrieval model.
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3.1 LIMITATIONS OF CROSS-LINGUAL INFORMATION RETRIEVAL IN PRACTICAL SCENARIOS
Figure 2 presents the evaluation results of cross-lingual retrieval performance using four different
multilingual embedding models, focusing on English as the source language and five target languages. The experiments are conducted under two distinct settings: (a) conventional CLIR, and (b) our proposed multi-reference cross-lingual setup, as defined in Section 2.2 Setting (b) features document pool with parallel documents in English and the target language, resulting in two ground-tron -tuut documents per query. To thoroughly investigate potential biases, we report the performance achieved when querying in both English and the respective target language. Based on these experimental results, we identify and discuss three critical limitations of existing multilingual embedding models and the CLIR environment that were not evident in conventional assessment settings.
Performance Disparities by Query Language One of the most significant limitations observed is the disparity in retrieval performance depending the query language. In Fig. 2 (a), the overall Max @R is notably low, and the performance differences by query language are relatively minor. In contrast, Fig. 2 (b) shows a substantial gap in performance based on the query language. For the multilingual-e) model, the results for Chinese (ZH) queries display a marked difference of 597.9 9 in Max Max@RR compared
the results for English queries. This finding indicates that cross-lingual semantic alignment is inadequate, resulting in amplified performance discrepancies based on the query language in the proposed scenario.
Representation Instability among Target Languages In CLIR, multilingual embedding models tend to exhibit relatively consistent performance across target languages. However, as shown in Fig. 2 (b), there is significant language-leve instability even within the same model. For example, for the ste-multilingual model, the Max R for Spanish (ES) remains relatively low and stable at 12.12, but rises sharply to 37.38 and 35.04 for Arabic and Thai, respectively, representing nearly a threefold increase. These results indicate that multilingual embedding models exhibit significant semantic representation inconsistency among target languages.
Excessively High Retrieval Ranks In Fig (b), most models and languages exhibit excessively high Max @R R values, making them impractical. Notably, for the multilingual-e5 with Chinese (ZH) queries, the Max @R R reaches 650.95, meaning that hundreds of documents must be reviewed to find all relevant documents. This result implies that the existing CLIR setting problems that emerge in practical scenarios, particularly the comprchensive issues of cross-lingual semantic alignment and retrieval capability.
fails to capture
In summary, the proposed scenario and metric have revealed critical issues that were overlooked in conventional settings. This shows that there is still significant room for improvement in multilingual embedding models regarding cross- mons-linguual aligmment and retrieval. This underscores the necessity for more realistic and rigorous evaluations across various cross lingull conditions, thereby providing clear direction for the advancement of multilingual embedding models.
METHODS
In Section we identified the issue of language misalignment within multilingual embedding models. Specifically, the identified problems and corresponding solutions are as follows: First, there is a lack of semantic alignment between text embeddings expressed in different languages, leading to an increased semantic distance between sentences and a decrease in overall retrieval performance. To resolve this cross-lingual semantic misalignment, we align the embedding distributions between English document (pen) and its semantically corresponding target language document (Ptgr) using a loss function based on Jensen- Shannon Divergence (JSD), explicitly optimizing semantic alignment in the embedding space. Second, to mitigate the language bias where retrieval results show an inclination towards documents in a particular language (primarily English), we employ InfoNCE contrastive loss by using positive pairs between English query(qen) and target language document (Pigt). To this end, we utilize dataset in the form of (qen,Pen: Pen. to train the model to minimize a combined loss of (1) JSD-based cross-lingual alignment loss LJSD and (2) InfoNCE contrastive loss LNCE, defined as follows:
L E E Pen gege [TJSDD LNCE]
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4.1 SEMANTIC EMBEDDING ALIGNMENT VIA LJSD
While standard CLIR objectives are designed to increase the similarity score between a query in one language and document in another, this approach may be insufficient for achieving robust semantic proximity. Achieving this requires more than just high similarity scores for retrieval; also necessitates that their underlying embedding representations are fundamentally aligned. As illustrated in Figure I representations produced by different objectives can share an identical cosine similarity score yet remain significantly misaligned at the distribution level. To align semantically equivalent passages, our goal is to minimize the divergence between the embedding distributions of texts expressed two different languages. A prominent method for measuring the disparities between two probability distributions is Kullback-Leibler (KL) divergence (Kullback & Leibler 1951). It quantifies how a distribution P diverges from another distribution Q and it is defined as follows:
(xi(P)1Q== = P. Q
(
However, KL-divergence is asymmetrie; that is, (KI(P)Q) # DKL(Q]1P). To address this asym- metry, Gensen-Shannow Divergence (JSD) is widely adopted. JSD first defines an intermediate averaged distribution M between two distributions and then measures the average KL--divergecce of each distribution from this intermediate distribution. It is defined as follows:
1 DKI((PP)) +
1 H DRIQUIM) M M (P+
JSD(PII) =
(4)
Divergence measures such as KL-divereeece and JS-Divergence are widely used to quantify the differences between predicted probability distributions and reference distributions. Previous studies typically focused on modeling the similarities between queries and documents as probability distribu- tions and minimizing the divergence between these predicted distributions and reference distributions. For example, in information retrieval tasks, model is trained to minimize the distributional diver- gence between a predicted distribution reflecting query-document similarities and a given reference distribution. In contrast, we propose directly aligming semantic embeddings at the distribution level. Specifically, semantic embedding vectors are interpreted as probability distributions. By explicitly aligning these embedding distributions using JSD), our approach aligns the embedding dimensions across languages, thereby effectively achieving enhanced cross-lingual semantic alignment. To illus- trate, let us denote the English document embedding vector as Zd... E Ridim and the corresponding target-language embedding vector as Zdage 12dim where gdim represents the embedding dimension. To transform these embedding vectors into categorical probability distributions over dimensions, we apply the following softmax function:
exp(z:) -im k==1 exp(zz)
P(z);
i= 1,2..... dim
(5)
After transforming the document embedding vectors into probability distributions P(za... and P(Zd.ge). ), we perform distributionn-lvel semantic embedding alignment by minimizing the JSD) between these probability distributions. The proposed loss function is:
min LJSD JSD (P((d..)).)) P(Zdegr) +
(6)
Finally, this loss function employs square root of the Lensen-Shammen divergence, a rigorous distribu- tional distance measure between embedding distributions of two languages. Specifically, taking the square root of JSD satisfies the three distance axioms (identity, symmetry. and triangle inequality). thereby forming a valid metric space (Endres & Schindelin 2003). Minimizing this distance- based loss during optimization encourages close alignment the dimension-level probabilistic structures of embeddings, facilitating more effective cross- rons-linggall semantic alignment.
4.2 RETRIEVING OBJECTIVE VIA LNCE
To improve similarity for cross-lingure query- -occumet pairs, we train the model to maximize the semantic similarity between an English query qen and its corresponding target language passage Pigt-
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We employ the InfoNCE loss, representative loss function of contrastive learning, as the objective function. Specifically, the loss for positive pairs (Pigt, 9 ;=1 is defined by the following equation:
min LNCE n1 Σ log exp(s(Pigt:>Ten. () + Ej exp( (8 (s(ptt, len.,))
1
exp(s Ton:))
where and m denote the total number of data and the batch size, respectively. The negative examples (unn,,y= are in-batch negatives (queries of other instances within the same batch). sp, q) the relevance score of p and q. measured by the cosine similarity between their respective representations. Through the optimization of this contrastive objective function, the semantic similarity between related pairs is maximized, while the similarity with unrelated examples is minimized. This process ultimately facilitates the semantic alignment within embedding space for cross-lingual retrieval.
EXPERIMENTS
In this section, we conduct comprehensive experiments across a variety of scenarios to evaluate the proposed method for cross-lingual alignment capability and retrieval performance.
5.1 EXPERIMENT SETUP
Scenario We design three experimental scenarios: (1) Multi evaluates whether the model can retrieve the two ground-truth documents per query in each language from a set of fully parallel documents in two languages.
(2) Multi-1 filters out only the specific same-language ground-truft for each query while retaining the rest of the parallel document pool. This forces the model to identify the corresponding document in the opposite language, evaluating
semantic retrieval capabilities under strict setting. (3) Mono assesses retrieval performance in single-language environment, further divided into cases where the query and document are in the same language (Mono-Sama Samo) and in different languages (Mono-Cross), with the latter corresponding to conventional CLIR. Through these scenarios, we evaluate the reliability of the model in both cross-lingual and monolingual settings.
Datasets To evaluate the three cross-lingual retrieval scenarios, it is essential use datasets that are fully parallel across languages. This fully parallel structure is a necessary prerequisite for rigorously validating our scenarios and the Max @R metric. This means that the same question-document pairs must exist in multiple languages to enable the evaluation of retrieval performance across different languages. For this purpose. we utilize the multilingual benchmarks XQuAD (Artetxe et al.) 2020) and Belebele (Bandarkar et al.) 2024),These These are high-quality, numan-translater benchmarks, widely adopted in standard multilingual retrieval evaluation benchmarks (aenokk///Eeoedeeennn et al. (2025). comprehensive description of these datasets and our rationale is provided in Appendix B Both datasets ensure complete parallelism, making them suitable for comprehensively validating performance across the proposed scenarios.
Metric We employ evaluation metrics that align with the characteristics of each retrieval scenario environment. For the Multi scenario, utilize Complete @ K (Qu et al. 2024), which considers an answer correct only if all relevant documents are included within the top-k results. This metric is reported as a percentage on 0-100 scale. Additionally, we use Max @ R, as proposed in Section 3 and introduce an intuitively interpretable and generalized metric, Max @ Rnomm which normalizes the varying Max @R values across different datasets on a logarithmic scale. This metric normalizes the maximum rank for each query to value between O and 100, represented as Max @ R
[o (log))))ao (Maz where DD is the size of the document pool, and |R] is the number of ground-truth documents for each query. For the Multi-l and Mono scenarios, where there only one correct document per query, we evaluate retrieval performance using metrics such as NDCG@1 (Järvelin & Kekäläinen, 2002) and MRR (Nogueira Cho 2019 Khattab & Zaharia 2020 Xiong et al. 2022 ( Karpukhin et al. 2020p. By employing appropriate metrics for each scenario, B we systematically compare and analyze the proposed method across various scenarios, thereby verifying its validity from multiple perspectives.
Φ Eveo! [100 [100

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Table 1: Main performance results under the Multi scenario
XQuAD
Belebele
Doe
Query
Comp@10 10
Max @ (1)
Max Rern
Comp@ 10 Ours
Max@ R (L)
Max Rasn
Base
Ours
Base
Ours
Base
Ours
Base
Base
Ours
Base
Ours
mulrillngual-e---
106.42
18.56
43.88
26.22
87.22
187.78
68.14
Ar
8.91
53.53
241.06
30,77
32.33
61.40
15:44
75.67
231,79
29.55
30.13
60.41
Ba+ZZ
En
29.24
61.60
53.04
17.35
53.71
69.50
63,44
86.78
44,74
15.96
54,31
69.47
650.95
23.10
18.31
3.11
82.44
476.45
19.54
67.26
En+Rs
En
58.99
65.63
19:83
15.38
67.61
71.19
68.67
88.89
49.14
13.44
52.94
71.99
Es
36.30
62.52
49:46
18.14
54.70
68.87
55,44
85.22
69.81
16.50
47.77
68.98
Ea+Th
En
24.37
57.23
69.94
18.81
49.81
68.35
41.11
87.33
112.49
15,44
40.76
69.95
Th
11,76
46.81
173.13
33.66
37.01
60.13
22 22
75.89
188.02
29.60
33:21
60.39
En+Vi
45.88
63.53
35.95
17.31
59.20
69.53
56.44
88.89
69.83
12.92
47.77
72.58
18.15
149.21
24.15
33.44
82.89
153.05
36.23
69.86
gfe-muddrlliggnal-Buss
EntAr
En
66.05
67.23
13.63
12.64
72,90
73.97
87 .44
88.56
10.03
9.17
76.29
77.62
Ar
49,58
52,10
37.38
31.38
58.65
61.12
79.67
80.33
26.23
23.55
62. 16
63.75
En+Zh
12.51
73,40
74.11
91
9.23
77.52
78.06
7h
58.82
63.11
17.73
15.24
69.19
71.32
86.67
88.56
13.93
12.80
71.47
72.71
En+Es
En
77.14
78.40
9.30
8.92
78.29
78.89
90.44
93.22
8.77
6.50
78.27
82.68
12.12
10.51
74.56
76.57
88.11
90.78
71.21
74.86
En+Th
En
64.96
67.48
13.83
13.02
72.69
73.55
88.67
89.33
9.97
9.03
76.39
77.84
Th
46.13
51.34
35.04
30.50
39.57
61.53
77.11
78.67
29.97
25.30
60.21
62.69
En+Ni
En
73.11
73.19
10.85
10.78
76.12
76.22
90.56
91.33
8.33
7.71
79:03
80.16
Vi
62.10
64.71
19:46
16.75
67.87
69.99
86.33
87.56
11.63
9.94
74.12
76.43
jlne-embeddlngs-3
EntAr
En
55.13
70.92
25.92
11.75
63.82
74.99
85,11 11
90.67
19.85
9.91
66.28
76.47
19.29
62.41
82.78
86.22
22.93
64.14
69.10
Ea+Zh
En
63.46
72.44
15.72
10.96
70.88
75.98
89.78
91.56
14.30
10.00
71.07
76.34
Zh
58.57
70.67
23.61
13,53
65.14
73.00
85,33
91.44
18.52
11.51
67.28
74.27
En+Es
En
75.63
19:92
10,10
67.54
77.14
85.11
92.22
24.16
9.88
63.38
76.52
Es
68.74
73,53
18.03
11.44
68.95
75.37
88.78
90.22
18.67
12,59
67.16
72.96
Fn
En
56.39
71.51
23.19
11.59
65.39
75.19
86.44
91.89
17.63
9.77
68.03
76.69
Th
58.07
68.32
25.28
16.41
64.18
70.28
84.33
86.67
22.17
15.78
64.65
69.63
En+Ni
En
59.75
71.43
24.99
11.69
64.34
75.07
83.00
91.89
22.91
9.91
64.16
76.48
61.51
67.06
25.42
18.64
64.10
68.48
87.33
90.67
19.88
12.27
66.25
73.33
bge-m3
19.66
67.73
13.78
71.62
76.08
Ar 63.71
67.98
18.96
17.39
68.24
69.46
85.11
85.44
18.91
15.82
66.98
69.60
Em+ZZ
En
67.82
71.09
15.14
13,08
71.42
73.48
89.44
90.56
12.14
10.90
73.49
75.07
63.19
14.99
13.01
71.56
73.56
88.67
90.00
12.65
72.88
74.20
Fn+Fs
Fn
77.90
78.82
9.88
9.36
77.4
78.21
91.78
93.00
10.25
9,29
75.98
77.43
Es
77.65
77.39
9.73
9.72
77.66
77.68
91.11
92.56
10.70
9.92
75.34
76.46
En+Th
En
67.90
71.34
14.25
12.67
72.27
73.93
90.89
92.11
10.24
9.03
75.99
77.83
Th
67.23
70.08
13.28
12.95
73.27
73.63
87.00
87.67
16.73
14.57
68.78
70.81
EntVi
73.33
90.56
75.59
77.93
Vi 66.72
70.92
15.06
13.40
71.49
73.14
89.00
90.78
11.01
9.80
74.93
76.63
Implementation Details The experiments are conducted on a total of 10 languages. In the main results, we report for five languages: Arabic (AR), Chinese (ZH), Spanish (ES), Thai (TH), and Vietnamese (VT). Detailed experiments for the remaining five languages, German (DE), Greek (EL), Hindi (HI), Romanian (RO), and Turkish (TU), are provided in the Appendix [D] We employ four multilingual embedding models for the experiments: multilingual-ESS
(Wang et al. 2024), gle- Multilingual-bas (Zhang et al. 2024), jina-embeddings-v3 (Sturua al., (2024), and bge-M3 (Chen) et al. 2024). We utilize the MIRACL train dataset (Zhang et al. (023b), which consists of 2.8k English query-documen, pairs. To obtain documents in each target language. the positive documents in English are translated into each target language using the GPT-4o (OpenAl 2024). Additional
details regarding the training procedure are provided in Appendix
5.2
MAIN RESULTS
In this section, we evaluate our method within multi-scenart where documents in English and the target language coexist, and each query has two gold documents. The results, presented in Table [ analyzed by querying in both English and the target language.
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Enhanced Cross-lingual Alignment The proposed method demonstrates clear and consistent improvements over baseline models across all evaluated language pairs and metrics. Notably, based on Complete @ 10, while baseline models exhibit relatively limited performance when queried in non-English languages, our proposed method achieves significant performance enhancements for all languages and models considered. These improvements indicate that method effectively facilitates semantic alignment among different languages within an embedding space. Ultimately, these results confirm that our method successfully addresses the limitations of existing multilingual embedding models, which are not captured by the conventional CLIR setting.
Reducing Variance in Language Bias In addition to improving retrieval performance, the proposed method reduces the issue of performance disparity between languages. Bascline models typically exhibit a bias toward English data, resulting in significantly reduced performance for non-English queries. However, the proposed method results in relative decrease in the quantitative performance disparity between English and target languages. For instance, the language performance gap for jina-embeddings-v? (En+Zh) consistently decreases from 6.89%p 1.77%p XQuAD and from 4.45%p + 0. 12% on Belebele. This result demonstrates that the model has mitigated the previous misaligmment between English and target languages, reducing language bias and contributing to improved language equity.
Enhanced Full-Recall Ranking Performance To intuitively assess cross-lingual alignment, we focus analyzing the Max @R metric in our experiments. The results show that our method consistently achieves significantly lower Max @R scores across languages and datasets compared to the baseline. For instance, with Chinese queries on the multilingual-e5-bie significantly improves from 650.95 to 23.10 on XQuAD. This indicates that our method effective in positioning relevant passages the top, demonstrating robust performance in cross-lingual multi- reference scenarios. These improvements are also observed by consistent gains on the Max @ Rnorm-
model, Max @R
5.3
CASE STUDY
Basel
Beselingish)
Dun[largst
[undEngiia))
Basel larget]
Baselenglish)
Durs(lrrgut
(uusjEngiaah
50
XQUAD
50
85-
BELEABLE
00
70
60
ZH
TH
2H
TH
(a) gte- moetilliilinnall- -bae base
(b) jin--ebeeddins--3
Figure 3: NDCG@1 comparison in the Multi-1 scenario with [lang] the query language
Cross-Lingual Information Retrieval in the Multi-1 Scenario The Multi-1 scenario provides a more rigorous assessment of cross-lingual semantic alignment capabilities. As shown in Figure 3 existing baselines encounter substantial difficulties in retrieving a relevant document written in languages different from the query language. In contrast, our proposed method consistently improves the NDCG@1 across all language pairs, demonstrating enhanced gross-lingual semantic proximity within the semantic embedding space. Furthermore, the observed performance gains are consistent and substantial, irrespective of whether the queries are in English in the respective target languages.
Performance Validation in Monolingual Settings To confirm whether the proposed method maintains robust retrieval performance in monolingual environments, conduct evaluations under two settings: Mono-Same and Mono-Crov The results, summarized Table demonstrate that our approach largely preserves modestly improves upon the baseline models in the Mono-Same
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Table 2: Mono scenario performance using jina-embecdding--33 English Target language queries.
XQuAD
Belebele
Lang
Model
Mono-Same
Mono-Cross
Mono-Same
Mono-Cross
NDCG@1
MRR
NDCG@1
MRR
NDCG@1
MRR
NDCG@1
MRR
Base
89.2/81.9
0.933/0.879
79.5/80.6
0.863/0.874
88.4/83.0
0.919/0.873
80.1/77.0
0.85640.833
Ar
Ours
90.8/83.9
0,945/0.898
87.1/81.9
0.919/0.884
88.4/87.0
0.922/0.871
82.6/79.8
0.830/0.856
Base
89.2/88.2
0,933/0.926
83.3/83.8
0.895/0.89 895
88.4/86.9
0.919/0.112
83.4/81
0.882/0.874
Zh
90.7/89.5
0.943/0.937
88.086.5
0.927/0.916
88.1/87.0
0.920/0.912
84.9/84.4
0.895/08896
Base
89.2/85.6
0.933/0.907
85.3/77.5
0.905/0.922
88.4/84.9
0.919/0.895
83.4/84.4
0.882018891
Es
Ours
89.2/88.8
0.936/0.931
89.3/88.0
0.935/0.925
87.7/85.4
0.918/0.898
85.6/84.7
0,901/0.892
Base
89.2/82.4
0.933/0.885
80.4/81.7
0.873/0.879
88.4/82.2
0.919/0.875
80.7/78.6
0.865/08844
Th
Ours
91.2/86.4
0.947/0.916
87.6/83.1
0.925/08994
88.7/82.2
0,923/08866
85.2/78.6
0.899/08448
Base
89.2/84.4
0.933/0.897
79.4/81.1
0.862/0.876
88.4/86.1
0.919/0.900
80.7/82.1
0.859/0.873
Vi
Ours
88.9/86.9
0.933/0.918
87.6/1.88
0.922M0884
87 37.6/85.0
0.914/0.897
83.4/83.6
0.889/1887
scenario. Notably, slight performance gains are observed for target language queries, suggesting that the quality of single language semantic representations is indirectly enhanced via our alignment method. In the Mono-Cross scenario, our method further surpasses the baseline models, providing consistent improvements. Collectively, these findings indicate that our approach does not compromise and sometimes even improves, monolingual retrieval performance. This affirms that reducing language misalignment in embedding spaces improves representational quality and retrieval performance in both cross cooss-lingual and monolingual settings.
5.4 ABLATION STUDY
In this section, we perform an ablation study to clearly examine the roles of two loss components in our method: the Jensen-Shannon divergence-basee embedding alignment loss LJSD and the InfoNCE-based query-document relevance learning loss LNGE. We also include comparative experiment, LNCEpag on enhancing the similarity between English documents (Pen) and target language documents (Ptgt) (Feng al. 2020; Chi et al. 2020).
Table 3: Ablation results for loss components in Multi scenario experiments the Belebele dataset. The results are reported in Max @ Raorm for both English and target language queries.
Methods
Ar
Zh
Es
Th
Vi
gte-mulillingul--eer
Baseline
76.29
77.52/ 71.47
78.27 71.21
76.39 / 60.21
79.03 74.12
LNCE
/3.45/63.28
76.07 71.72
79.33 74.13
76.34/ 72.54/62.53 62.53
77.79 74.77
Ours
77.62 / 63.75
78.06 / 72.71
82.68 / 74.86
77.84/ 62.69
80.16/ 76,43
w/o Lisu
75.63 / 63.19
75.18/711 72
80.43 74.14
74.75 66.93
78.99 / 75.39
LNCEE
76.14
70.09
82.00/ 71.666
77.13/59.66
jina-embeddings-33
Baseline
66.28 / 64.14
71.07 / 67.28
63.38 / 67.16
68.03 / 64.65
64.16/ 66.25
LNCE,
72.37 68.12
75.00 72.92
68.06
72.35 68.66
70.52/ 70.46
Ours
76.47 69.10
76.34 / 74.27
76.52 / 72.96
76.69 69.63
76.48 73.33
wlo Lasu
71.58/ / 67.73
74.64 / 72.96
68.31 69.88
71.90 68.19
70.42. 70.19
/
57.44
15.47
Results in Table 3 indicate clearly complementary roles for these components. Specifically, the absence LJSD negatively affects cross-lingual semantic embedding alignment and overall retrieval performance, whereas excluding LNCE limits the retrieval effectiveness, despite embedding-leve alignment. This demonstrates that the combination of LJSD and L is essential for effectively achieving objectives of semantic alignment within embedding space and securing retrieval perfor- mance. It also proves that relying solety on one of these components insufficient to enhance overall cross-lingual retrieval.

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Additionally, we analyze LNCE The results show that this approach generally improves retrieval performance over the Baseline. confirming that enhancing document- level similarity is a valid strategy for improving cross- monss-linuall representations. Crucially, regardless of the base model, Ours consistently and significantly outperforms the E,,, approach. This result highlights the source of our method's superiority: rather than simply increasing similarity score between documents, it provides more fundamental solution by directly aligning the distribution of the output representations themselves, making it more effective for the end task of query-document retrieval.
RELATED WORKS
Most existing studies Eross-Lingual Information Retrieval (CLIR) focus on bridging the semantic gap by constructing cross-lingual embedding spaces or leveraging knowledge transfer approaches to minimize query-document distances across languages (Huang et al.] 2023a Yu et al. 2021: Litschko
et al. 1 Valentini et al. 2025 Lin et al., 2023). Unsupervised methods, in particular, have
attempted to reduce dependencies on translation resources by training shared embedding spaces only from monolingual corpora (Litschko et al.) 2018). Concurrently, several studies address low-resource languages, where parallel corpora are limited, by proposing optimal transport- based knowledge distillation or multi-stage knowledge distillation techniques to transfer ranking knowledge from high-resource languages (Huang et al. 2023a b). Additionally, integrating knowledge graphs into query-document representations has shown promise in alleviating cross-lingual semantic gaps (Zhang ct al. 2022: Litschko et al., (002)) Collectively, these studies underscore that aligning and refining cross- Loogsllingal embedding representations is critically important for CLIR. However, most prior studies assume either purely monolingual or entirely multilingual document pool, thus failing adequately address biases and misalignments that arise when two languages coexist in a single document pool.
In parallel, studies have increasingly focused on explicit approaches for aligning multilingual em- bedding spaces. [Hu et al. (2020) leverages parallel corpora and explicit alignment objectives to enhance sentence-level cross-lingual transferability. More granular studies addressing contextual embedding alignment have introduced nuanced evaluation tasks, such as dependency parsing or token-level semantic retrieval, highlighting the importance of fine-grained measures (Schuster et al., 2019: Liu et al., 2019). While insightful, these works primarily emphasize alignment in general representation tasks, offering limited consideration of practical retrieval challenges associated with combined-langee language environments.
7 CONCLUSION
To investigate severe semantic misalignment and language disparities exhibited by existing multilin- gual embedding models, we propose a new evaluation scenario and a metric, Max @R. Through our experiments, we reveal previously unseen issues that were not observable using existing evaluation scenarios. To address these issues, we present a training strategy that effectively achieves semantic proximity in the cross-lingual embedding space by leveraging Jensen-Shannon Divergence for se- mantic embedding alignment and InfoNCE for enhancing gross-lingual retrieval performance. Our method mitigates linguistic misalignment and language bias, significantly improving -ingull retrieval performance and effectively reducing performance disparities across languages. Additionally, our method demonstrates stable performance even in monolingual settings.
ETHICS STATEMENT
This study proposes a cross-lingual embedding alignment methodology that can positively impact applications requiring effective information retrieval across multiple languages. The proposed method accurately aligns semantic representations across different languages to enhance retrieval performance, while simultaneously strengthening the generalization and robustness of multilingual embedding models. In particular, our approach is applicable even to low-resource languages, potentially miti- gating global information disparity and contributing to the creation of a more equitable information access environment. Moreover, by effectively utilizing existing large-scale scale embedding models and data resources, our approach significantly reduces additional costs associated with data construction and labeling. However, because the training process involves translations generated by large language
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models, there potential risks of subtle cultural nuances being distorted the introduction of data biases, which may lead to inaccurate or unintended outcomes for certain cultural or linguistic groups. This study transparently acknowledges and carefully discusses these limitations and risks. Nevertheless, we firmly believe that the expected benefits and positive impacts of our research substantially outweigh the aforementioned concerns.
REPRODUCIBILITY STATEMENT
Our research designed for full reproducibility. Details on the experimental setup, including datasets and implementation specifics, are described in Section 5.1 Further training information, such as the specific hyperparameters used, can found in Appendix For the computational resources required to run our experiments, please refer to Appendix
ACKNOWLEDGMENTS
This research was supported by Basic Science Research Program through the National Research Foundation of Korea(NRF) funded by the Ministry of (Noiatioon(NR-22222 20122022110 IR1 A6A A03045425). This work was supported by Institute for Information communications Technology Promotion(IITP) grant funded by the Korea government(MSIT) (RS-2024-003981 15, Research on the reliability and coherence of outcomes produced by Generative AI). This work was supported by Institute of Informa- tion & communications Technology Planning & Evaluation (ITP) under the artificial intelligence star fellowship support program to nurture the best talents LITP-2006-RP --0-RRR2222-0224442222 grant funded by the Korea govermmen(MSSTT). This work was supported by ICT Creative Consilience Program through the Institute of Information & Communications Technology Planning & (valuation(IIIPP) grant funded by the Korea goVernmen((MSIT) HITP-202--RS-00201120533
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Xin Zhang, Yanzhao Zhang, Dingkun Long, Wen Xie, Ziqi Dai, Jialong Tang, Huan Lin, Baosong Yang. Pengjun Xie, Fei Huang, Meishan Zhang, Wenjie Li, and Min Zhang. mGTE: General- ized long-context text representation and reranking models for multilingual text retrieval. In Franck Demoncourt, Daniel Preotiuc-Pietro and Anastasia Shimorina (eds.), Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing: Industry Track, pp. 1393-1412 Miami, Florida, US, November 2024. Association for Computational Linguistics. doi: 10. emn
URL https: fttt:s://accaanthon otr.lanttooooooo.oo/22220222
Xinyu Zhang, Kelechi Ogueji, Xueguang Ma, and Jimmy Lin. Toward best practices for training multilingual dense retrieval models. ACM Transactions Information Systems, 42(2):1-33,
2023a.
Xinyu Zhang, Nandan Thakur, Odunayo Ogundepo, Ehsan Kamalloo, David Alfonoo- Xiaoguang Li, Qun Liu, Mchdi Rezagholizadeh, and Jimmy Lin. MIRACL: A multilingual retrieval dataset covering 18 diverse languages. Transactions of the Association for Computational Linguisticss 1 11:1114-133, 2023b. doi: 10. 162/tacl a 00595. URL htap//aaantttttt anthooogy org/2023. t.c1-1.637
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A
LIMITATIONS
major limitation this study arises from the experimental setup and evaluation process for cross- lingual alignment, which are primarily centered around English despite the extensive possibilities of language combinations.
real-world scenarios, various language pairs that do not involve English frequently cxist, requiring consideration. Nonetheless, experiments and evaluations in this study were intentionally centered around English, the most widely used and high-resouree language, under the assumption that multilingual models would exhibit significant bias toward alignment with English, and to reflect the most practical real-world scenarios.
Additionally, although evaluations considered various scenarios (Multi, Multi- 1, Mono) assess cross- wross-lingual retrieval performance, the current experimental design focused mainly on two languages settings. In practice, multilingual contexts involving more than languages frequently occur. However, we restricted our evaluations primarily to cross-lingual settings because existing models still struggle with performance even in these simpler setups.
Moreover, our approach utilized machine translation based on language models to build the training dataset. Compared human translation, automatically translated data may fail capture subtle linguistic nuances and cultural contexts sufficiently Toral & Way (2018); Läubli et al. (2018); Lee al. (2024). However, we adopted this methodology as it currently represents the most practical and efficient solution available for constructing large-scale multilingual datasets.
B EVALUATION BENCHMARK DETAILS
In this study, we utilize multilingual Question- Cantiion-nnserring (QA) datasets with parallel structures, converted into retrieval tasks, to evaluate cross-lingual retrieval performance. Specifically, we use XQuAD a multilingual question-answer dataset derived from SQuAD 1.1 Rajpurkar et al. (2016). which consists of fully parallel question-answer pairs across 13 languages including English. XQuAD was directly translated by professional translators, ensuring precise one-to-one correspondence between documents and queries across languages. This high-quality translation process naturally preserves linguistic expressions and semantic meanings in each target language, making XQuAD particularly suitable for assessing the robustness embedding models against language variations in cross-lingual scenarios. Additionally, utilize Belebele"] another multilingual QA dataset that includes diverse language pairs. Belebele was carefully translated from English into multiple languages by native-speaking translators proficient in English, effectively capturing contextual nuances and cultural subtleties Bandarkar et al. (2024). Due to these characteristics, Belebele provides realistic and varied scenarios reflecting practical multilingual retrieval environments, enabling fine- grained comparison and analysis of retrieval performance across languages. Furthermore, our use of these specific benchmarks aligns with standard practices for robust multilingual evaluation. Both XQuAD and Belebele are core benchmarks used for assessing multilingual retrieval capabilities in the prominent Massive Multilingual Text Embedding Benchmark (MMTEB) Enevoldsen et al. (2025
C TRAINING DETAILS
Dataset Translation We employed Large Language Model to translate datasets for training purposes. The format template for translation prompts used as inputs to the model is as follows:
System: #Instructions
Translate the following English passage fully and accurately into (target language)
User:
(English passage) Assistant: (Transiated passage)
ebell
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Hyperparams All models trained for total of one epoch with batch size of 32, em- ploying a linear learning rate scheduler with a warm-up ratio 0.15. We used the AdamW opti- mizer (Loshchilov & Hutter 2019) (with parameters 31 0.9, 32 0.99, and weight decay=0.01), and adopted 1 mixed precision for computational efficiency. Considering the characteristics of each model, large, le-5 for cee-muliiinguallbasse and 3e-5 for jin--embedding-v3.
the initial learning rates as follows: 4e-6 for bge-M3, 2e-5 for multilingual-ES.
Hardware We used 2 NVIDIA A100 GPUs, each with 80GB memory capacity, along with AMD EPYC 7513 processors featuring 32 cores, to train models. For evaluation, we employed single accelerator.
Reproducibility To ensure fair and consistent comparison, all experimental results reported in this paper are based on a single run using a fixed random seed (42). This fixed seed controls all stochastic elements, including data shuffling and the batch composition and sampling order. This approach allows us to strictly control the training environment for both baselines and our proposed model, ensuring that the comparison fairly isolates the effect of our methodology.
D EXTENDED RESULTS FOR ADDITIONAL LANGUAGES
In this section, we provide experimental results for three scenarios involving five languages previously included in the main body of the paper: German (DE), Greek (EL), Hindi (HI), Romanian (RO), and Turkish (TU).
Oustlnggin
62-
80
R0
(a) gte- me-lilltilnguall- base
(b) mana-embeddings--3
Figure 4: NDCG@1 comparison on additional language pairs in the Multi-1 scenario with [lang] as the query language.
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Table 4: Performance comparison on remaining language pairs under the Multi scenario.
XQuAD
Belehele
Doc
Query
Comp@ 10
Max@R R (1)
MaxoR-norm
Comp@ 10
Max@ R (L)
Max@ R-morm
Base
Ours
Buse
Ours
Buse
Ours
Base
Ours
Base
Our
Base
Ours
mulrilingual-eS--
Ea+De
54.20
24.16
64.82
66.89
52.44
71.41
De
39.41
62.35
45.38
21.00
55.91
66.80
53.00
86.44
70.03
16.05
47.73
69.38
En+EI
En
24.45
62.10
63.15
17.59
51.25
69.30
33.67
86.56
140.91
15.18
37.45
70.20
7.73
53.18
248.40
30.10
31,91
61.71
11.78
76.78
287.99
22.93
26.94
64.14
En+Hi
En
46.97
62.61
26,87
18.84
63.32
68.33
65.33
87.89
47.43
14.08
53.46
71.31
Hi
26.55
53.70
101.12
30.74
44,00
61.41
45.78
76.00
86.64
27.37
44.60
61.54
EntRo
53.61
15.57
71.03
64,22
55.41
51.17
70.47
Ro
23.28
61.01
89.76
24.89
46.28
64.40
39,56
82.44
145.65
20.94
39.96
65.48
En-Tu
42.44
62.69
35.54
17.82
59.37
69.12
59.11
87.51
10.04
7.51
76.28
80.54
24.20
94.54
30.75
45.55
61.41
44.22
80.00
126.08
39.08
62.91
gte-mudiillngguat-boo
En+De
En
75.13
75.71
10.45
9.86
76.65
77.48
88.56
92.67
8.79
6.99
78:23
81.61
De
67.90
71.09
16.27
14.08
70.40
72.45
87.67
89.22
13.30
9.79
72.15
76.66
En-Ell
67.14
11.95
10.9%
74.75
75.95
88.67
90.22
10.34
75.85
78.84
51.68
58.71
25.08
20.28
64.29
67.29
81.00
84.33
24.88
18.72
62.94
67.12
En+Hii
Ea
71.93
72.27
12.07
11.52
74.61
75,28
89.00
91.67
9.35
8.82
77.33
78.18
54.96
58.66
27.76
23.51
62.86
82.11
84.00
23.81
63.59
66.40
Ea+Ro
En
73.70
76.47
11.04
9.60
75.88
77.85
89.22
92.89
9.28
7.19
77.44
81.20
Ro
64.54
67.56
16.94
14.13
69.83
72.40
84.89
88.22
17.44
12.57
68.17
72.97
12.82
11.03
73,76
90.22
92.33
10.04
76.28
Tu
$5.29
61.51
26.18
20.31
63.68 63.68
67.27
83,22
86.89
19.65
14.53
66.41
70.85
jine-embedddings-22
Eo
En
69. 50
75.80
15.12
10.08
71.44
77.16
89.00
92.89
14.25
9.00
71.
77.88
72.86
13.94
11.08
72.58
75.82
91.33
92.33
74.75
77.41
En-FI
En
53.03
71.76
31.84
11.98
60.93
74.73
80.56
89.22
36.69
13.11
57.21
72.35
EI
57.31
67.31
27.76
16.49
62,86
70,21
83,22
87.33
32,88
22.58
58.83
64.37
61.93
20.27
67.30
87.56
91.67
20.73
65.62
74.74
Hi
62.88
69.58
24.82
16.52
64.43
70.19
85.00
86.67
28.77
20.32
60.83
65.92
Fi
64.54
74.20
24.53
10.65
64.63
76.38
83,22
92.22
31.87
10.17
59.30
76.10
19.67
12.90
67.73
73.68
86.78
91.00
25.46
14.12
62.59
71.27
En
En
62.44
73.87
21.33
10.54
66.58
76.53
86.67
93.00
18:45
9.39
67.35
77.27
65.71
71.60
18.89
13.19
68.28
73.36
87.56
89.89
18.65
12.71
67.19
72.82
bge-m.3
10.02
9.96
77.24
93.78
77.25
78.66
De
76.22
75.97
10.14
10.44
77.08
76.67
92.22
93.22
8.44
8.12
78.83
79.41
En+El
En
68.24
72.94
13.62
12.11
72.91
74.58
89.33
91.67
11.11
9.73
74.79
76.75
13.23
13.23
73.32
73.32
86.22
17.75
67.91
71.38
En+Iti
En
69.66
72.86
11.96
11.55
74.75
75.24
89.89
91.44
11.10
10.04
74.80
76.29
Hi
68.07
70.42
13.69
13.19
72.84
73.36
83.11
85,89
21.98
19.88
64.76
66.24
Ea+Ro
78.07
78.32
9.97
9.66
77.31
77.75
92.67
93.22
9.66
8.56
76.84
78.63
Ro
74.03
75.13
11.66
11.17
75.10
75.71
91.22
92.67
10.13
9.41
76.15
77.23
En-Tu
73.28
74.79
11.48
11.25
75.32
75.61
91.56
92.11
9.52
8.46
77.07
78.79
34
13.38
12.33
73.16
74.32
89.56
90.78
13.82
71.58
73.96
Table 5: Performance comparison under the Mono scenario for five additional languages using the aina-embeddings-v v3 model: English/ / Target language queries.
XQuAD
Belehele
Lang
Model
Mono-Same
Mono-Cross
Mono-Same
Mono-Cross
NDCG@
MRR
NDCG 1
MRR
NDCG@
MRR
NDCG@
MRR
Base
89,2/90.0 0.933/0.937
87.8/87.
0.923/0.925
$8.4/89.3
0.919/0.926
84.9/86.0
0.897/0.903
De
Ours
90.6/88.4
0.943/0.929
89.1/88.9
0.933/1.32
87.6/88
0:916/0.916
86.8/66.3
0.909/1990
Base
89.2/80.5
0.933/1873
1.7
0.863/0.884
88.4/83.6
0.919/0.883
80.1/78.3
0.852/0.841
Ours
90.0/86.8
0.940/0.916
88.4/83.5
0.928/0.895
88.8/84.0
0.924/0.887
83,4/81.1
0.883/0.864
Hi
Base
89. 29.23/3.3
0.933/0.891
81. 11.6/84.44
0.883/0.899
88.4/81.1
0.919/0.860
82.3/79.4
0.875/0.852
Ours
91.6/87.5
0.950/0.922
89.0/85.2
0.933/0.907
89.1/82.2
0.926/0.872
86.0/79.4
0.902/0.857
Base
89.2/84.7
0.933/0.899
82, 4/855.3
0.885/0.999
88.4/85.1
0.919/0.892
81.7/82.6
0. (.8699).89 875
Ro
Ours
88.7/86.5
0.932/0.917
1/81/8
0.927/0.908
87.2/87.0
0.914/0.907
84.8/85.2
0.895/0888
Base
89.2/84.9
0.933/0.90 901
82, 32.2/84..
0.883/0.900
88.4/85.9
0.919/0.896
82,0/82.2
0.874/0.874
Ours
89.5/88.6
0.938/0.928
89.1/85.2
0.935/0.907
88.4/86.7
0.921/0.908
87.0/84.9
0.91191.82
17


Reviews:
[{"review_id": "DLPOiIZL9l", "reviewer": "Reviewer_oW6S", "summary": "In the paper, the authors revisit the Cross Lingual Information Retrieval (CLIR) task through the lens of cross-lingual alignment evaluation. They argue that in a CLIR setup, the English unrelated documents, for a given query, are prioritized in the ranked list when compared with the related documents in the same language as the query. To alleviate this, they have proposed both new metrics and training setups, testing on standard benchmarks. The results show that the proposed setup achieves better semantic alignment in the CLIR task across many languages.", "questions": "The motivation for a new metric needs clearer motivation.\n\nMore baselines, as already suggested, will further fortify the claim of the paper.", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "The problem is clearly described. The formal presentation of the problem premise in Section 2 makes it easy to comprehend.\n\n* The demonstration through Performance Disparities, esp. for Chinese, is insightful.\n\n* The authors show semantic Representation Instability among languages. They show that the performance is worse for Arabic and Thai when compared with Spanish.\n\n* The authors also show high retrieval ranks for many languages. \n\nThese three key findings establish the motivation for a better setup.\n\nBased on the premise, the authors propose semantic alignment techniques to ensure better alignment between the query and the relevant English document. This is proposed by the application of InfoNCE between the query and the relevant document. JSD is deployed to minimize semantic differences between embedding vectors.\n\nThe authors report experiment results on datasets: XQuAD and Belebele using metrics like Complete@K. In total, 10 languages have been considered.\n\nThe results show increased cross-lingual alignment, showing the efficacy of the proposed training setup. \n\nIn section 5.4, the superior performance over LNCE is reported.\n\nMost of the standard embeddings, like multilingual-e5, gte-multimodal, bge, etc., have been considered.", "weaknesses": "It is not clear why the existing metrics, like MAP, can not be used for CLIR evaluation. The new metric Max@R, needed a clearer comparison with existing IR evaluation platforms in the CLIR setup. The authors need to establish better why a new metric was needed and how the existing metrics (even with customization for the premise of the paper) were inadequate.\n\nThe setup \"We report the performance achieved when querying in each language within these environments.\" (line 159-160) need more details for reproducibility and because, importantly, the claims in this section hinge on the reliability of these results. I will be curious to also look into word-overlap-based and yet effective baselines like BM25 in the CLIR setup, empowered with translation resources like https://github.com/facebookresearch/fairseq/tree/nllb (No Language Left Behind: Scaling Human-Centered Machine Translation).\n\nThough a little older, I would still suggest comparison with Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks (sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2 and other multilingual variants).", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "8lU581xENG", "reviewer": "Reviewer_dUrW", "summary": "This paper introduces a new training strategy to enhance cross-lingual alignment in information retrieval, addressing the issue of language bias and semantic misalignment in multilingual document pools. The authors propose a method that combines Jensen-Shannon Divergence (JSD) and InfoNCE losses to improve semantic alignment and retrieval performance. By aligning semantic embeddings across languages and optimizing retrieval capabilities, their approach significantly reduces language bias and improves retrieval accuracy. Experiments across multiple languages and datasets demonstrate the effectiveness of the proposed method, showing substantial improvements in cross-lingual retrieval performance and reduced bias, thus providing a robust solution for enhancing semantic proximity in multilingual information retrieval.", "questions": "Please refer to the “Weakness” section for related questions.", "limitations": "", "rating": 4, "confidence": 2, "soundness": 2, "presentation": 2, "contribution": 2, "strengths": "1. The writing of this work is relatively clear.\n2. The experimental validation in this work is relatively comprehensive.", "weaknesses": "1. In the section “Introduction”, why does this paper deliberately emphasize English rather than other languages?\n\n2. In the section “Related work”, ① The description \"However, most prior studies assume either purely monolingual or entirely multilingual document pool, thus failing to adequately address biases and misalignments that arise when two languages coexist in a single document pool.\" seems ambiguous. The focus of this paper should also be on multilingual issues, and I am not clear about the differences between the current description and the methods mentioned. ② The descriptions \"While insightful, these works primarily emphasize alignment in general representation tasks, offering limited consideration of practical retrieval challenges associated with combined-language environments\" are vague and inaccurate, and the authors are advised to clarify them. ③ I also noticed that some works [1][2] seem similar to this paper, but they are not introduced or reviewed. What are the differences between [1] and this paper?\n\n3. In the section “Method”, how is the positive sample pair defined?\n\n**Reference**\n\n[1]  Zuo Y, Jiang W, Liu W, et al. Alignxie: Improving multilingual information extraction by cross-lingual alignment[J]. arXiv e-prints, 2024: arXiv : 2411.04794.\n\n[2]  Kargaran A H, Modarressi A, Nikeghbal N, et al. MEXA: Multilingual evaluation of English-centric LLMs via cross-lingual alignment[J]. arXiv preprint arXiv:2410.05873, 2024.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "s2c4DrWlem", "reviewer": "Reviewer_gRdR", "summary": "The paper addresses challenges in Cross-Lingual Information Retrieval (CLIR) and Multilingual Information Retrieval (MLIR) tasks where multilingual embeddings often exhibit biases, especially toward English.The authors introduce a new evaluation metric (Max@R) and propose a training strategy combining Jensen-Shannon Divergence (JSD) and InfoNCE contrastive loss to improve the cross-lingual alignment of embeddings. The method demonstrates significant improvements in retrieval performance even with a small dataset of 2.8K samples. Experiments on XQuAD and Belebele datasets across 10 languages and 4 models demonstrate consistent improvements and reduced language bias disparities.", "questions": "- Why is JSD in Equation 6 superior to alternative divergence measures for the retrieval task?\n\n- In Table 1, the Comp@10 values range from approximately 0.5 to 93.22 (e.g., multilingual-e5-base En+Zh: 0.50 vs. gte-multilingual-base En+Ar: 87.44). Are these values reported as percentages (0–100 scale), or as proportions (0–1 scale)?\n\n- Additionally, was the result averaged across multiple runs? If so, how many independent experiments were performed, and how were random seeds selected and fixed? Please provide clearer or additional explanations to improve reproducibility.", "limitations": "", "rating": 4, "confidence": 3, "soundness": 2, "presentation": 2, "contribution": 2, "strengths": "- 1. Proposing a cross-lingual embedding optimization method that combines JSD and InfoNCE, which is a novel and reasonable approach;\n\n- 2. The experiment covers multiple metric and text embedding models.The experimental results are sufficient and the analysis of the experimental results is also very convincing.\n\n- 3. Using only 2.8k samples to improve multiple pre-trained models is efficient and suggests the method is data-efficient for practitioners with limited parallel resources.", "weaknesses": "- 1. In the methodology section, especially the description of the JSD semantic alignment process is too brief, and readers may need diagrams to understand the idea of distribution level alignment. Equation 6 is motivated only by satisfying distance axioms, but many divergence measures satisfy these properties. The paper does not explain why JSD over, say, Wasserstein distance, Bhattacharyya divergence, or direct embedding-space alignment (e.g., via cosine similarity matching or optimal transport on embeddings) would be superior.\n\n- 2. The lack of a more detailed description of the dataset has affected the intuitiveness of the experimental results. XQuAD and Belebele are QA datasets converted to retrieval by treating passages as documents. This is somewhat artificial; natural retrieval corpora with mixed languages would be more convincing.\n\n- 3. More detailed explanations are needed for some metric, such as numerical ranges, numerical size meanings, etc.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "YM9pcLeI0m", "reviewer": "Reviewer_Mk7b", "summary": "This paper tackles English‑centric bias and poor cross‑lingual alignment in multilingual retrieval models. When English and non‑English documents coexist, models often favor irrelevant English content. To address this, the authors propose a mixed‑language evaluation setup, a new metric Max@R to measure alignment quality, and a joint training strategy combining Jensen–Shannon Divergence for embedding alignment with InfoNCE for contrastive retrieval learning. Experiments on XQuAD and Belebele show significant performance gains.", "questions": "See the Weaknesses.", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "- The authors identify a critical limitation in existing CLIR models: when retrieving from multilingual document pools, multilingual embedding models tend to prioritize irrelevant English documents while overlooking relevant ones written in the same language as the query.\n- The authors propose a new evaluation metric, Max@R, to measure retrieval performance more effectively in mixed-language document environments.\n- The authors present an optimization approach that jointly uses embedding alignment and contrastive learning, which is straightforward and effective.\n- Experimental results demonstrate the effectiveness of the proposed approach.", "weaknesses": "- The construction of alignment samples relies on translations generated by large language models, which may introduce translation bias or noise into the training data.\n- The constructed dataset contains only 2,800 samples, which may lead to potential overfitting of the model.\n- The proposed method is primarily trained with English queries. It is recommended to further explore retrieval performance using queries in a broader range of languages.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (Poster)