Papers with monolingual setting
Sõnajaht: Definition Embeddings and Semantic Search for Reverse Dictionary Creation (2024.starsem-1)
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| Challenge: | Existing systems that use exact term matching to find words are based on information retrieval. |
| Approach: | They propose to use pre-trained language models and approximate nearest neighbors search algorithms to enhance and enrich an Estonian lexicon resource by introducing cross-lingual reverse dictionary functionality powered by semantic search. |
| Outcome: | The proposed system produces a 1 and 2 routs in the monolingual and cross-lingual settings using the unlabeled evaluation approach. |
A Dataset and Baselines for Multilingual Reply Suggestion (2021.acl-long)
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| Challenge: | Reply suggestion models help users process emails and chats faster. |
| Approach: | They present a multilingual reply suggestion dataset with ten languages . they build a generation model and a retrieval model as baselines for MRS . |
| Outcome: | The proposed model complements existing benchmarks for cross-lingual generalization . the model has different strengths in the English monolingual setting and requires different strategies to generalize across languages. |
Bilingual Tabular Inference: A Case Study on Indic Languages (2022.naacl-main)
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| Challenge: | Existing studies on Tabular Natural Language Inference (TNLI) focus on monolingual settings where tabular premise and hypothesis are in the same language. |
| Approach: | They propose a task where tabular premise and hypothesis are in two languages . they translate textual hypotheses from an English-indic TNLI dataset into eleven major languages - english and indic . |
| Outcome: | The proposed model performs well on a bilingual dataset in English and in 11 major Indian languages. |
Adapting Word Embeddings to New Languages with Morphological and Phonological Subword Representations (D18-1)
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| Challenge: | Existing approaches to generalization to resource-rich languages are difficult . a recent study shows that word representations can be useful in low resource languages . |
| Approach: | They propose two approaches for improving generalization to low-resource languages by adapting continuous word representations using linguistically motivated subword units. |
| Outcome: | The proposed method improves generalization to low resource languages . it requires neither parallel corpora nor bilingual dictionaries and requires no parallel training . |
Target-Oriented Relation Alignment for Cross-Lingual Stance Detection (2023.findings-acl)
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| Challenge: | Existing work on cross-lingual stance detection has ignored the inconsistency in the occurrences and distributions of targets between languages, which consequently degrades the performance of stance detector in low-resource languages. |
| Approach: | They propose a fine-grained method which considers both target-level associations and language-level alignments to learn the in-language and cross-language associations. |
| Outcome: | The proposed method is compared with competing methods under variant settings and shows that it performs better in low-resource languages. |
Spectral Probing (2022.emnlp-main)
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| Challenge: | Contextualized embeddings capture linguistic phenomena at distinct layers and frequencies . current studies rely on handcrafted filters and are limited to English . |
| Approach: | They develop a fully learnable frequency filter to identify spectral profiles for any given task. |
| Outcome: | The proposed filter enables more granular analyses than handcrafted filters and improves efficiency. |
Breaking the Language Barrier: Improving Cross-Lingual Reasoning with Structured Self-Attention (2023.findings-emnlp)
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| Challenge: | Recent studies show that multilingual language models (MultiLMs) are capable of logically reasoning over natural language statements, reasoning with their implicit knowledge, and performing multi-step reasoning when the model size is large enough. |
| Approach: | They propose a mechanism that encourages cross-lingual attention in code-switched sequences and improves reasoning performance by up to 14%. |
| Outcome: | The proposed approach improves reasoning performance by 14% and 4% on the RuleTaker and LeapOfThought datasets. |
BAN-Cap: A Multi-Purpose English-Bangla Image Descriptions Dataset (2022.lrec-1)
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| Challenge: | BAN-Cap dataset is based on the widely used Flickr8k dataset, which is used to collect captions of images from qualified annotators. |
| Approach: | They propose to use a dataset to collect Bangla captions from qualified annotators and to evaluate the models for the task. |
| Outcome: | The proposed model outperforms state-of-the-art models for Bangla captioning and English-Bangla translation. |
How Programming Concepts and Neurons Are Shared in Code Language Models (2025.findings-acl)
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| Challenge: | Several studies have focused on programming languages in a monolingual setting, but most focus on programming language models. |
| Approach: | They perform a few-shot translation task on 21 PL pairs using two Llama-based models and decode the embeddings of intermediate layers. |
| Outcome: | The proposed model assigns high probability to English tokens in the second half of the intermediate layers and language-specific neurons are concentrated in the bottom layers . the model's concept space is closer to English (including PL keywords) and the model is more efficient at identifying language-related neurons. |
Authorship Attribution in Multilingual Machine-Generated Texts (2026.acl-long)
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| Challenge: | Large Language Models (LLMs) have reached human-like fluency and coherence, but distinguishing machine-generated text from human-written content becomes increasingly difficult. |
| Approach: | They propose a problem of multilingual authorship attribution (AA) that involves attributing texts to human or multiple LLM generators across diverse languages. |
| Outcome: | The proposed method can be adapted to multilingual settings, but still has significant limitations and challenges. |