Papers with MRL
A Truly Joint Neural Architecture for Segmentation and Parsing (2024.eacl-long)
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| Challenge: | Contemporary multilingual dependency parsers can parse a diverse set of languages, but performance is lower for Morphologically Rich Languages. |
| Approach: | They propose a joint neural architecture where a lattice-based representation is provided to an arc-factored model and solves the morphological segmentation and syntactic parsing tasks at once. |
| Outcome: | The proposed architecture is language-agnostic and language-based to improve on Hebrew . it shows that the proposed model can parse morphological segmentation and syntactic parsing tasks at once. |
MolTC: Towards Molecular Relational Modeling In Language Models (2024.findings-acl)
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Junfeng Fang, Shuai Zhang, Chang Wu, Zhengyi Yang, Zhiyuan Liu, Sihang Li, Kun Wang, Wenjie Du, Xiang Wang
| Challenge: | Molecular Relational Learning (MRL) is a promising way to understand interactions between molecular pairs. |
| Approach: | They propose a novel LLM-based multi-modal framework for molecular interaction modeling following Chain-of-Thought (CoT) theory which integrates graphical information of two molecules in pair. |
| Outcome: | The proposed framework integrates graphical information of two molecules in pair. |
Representations and Architectures in Neural Sentiment Analysis for Morphologically Rich Languages: A Case Study from Modern Hebrew (C18-1)
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| Challenge: | Existing sentiment analyzers for MRLs that use tokens and morpheme-based representations have no empirically studied effects of representation choices on neural sentiment analysis. |
| Approach: | They develop a sentiment analysis benchmark for Hebrew based on 12K social media comments and provide two instances of data. |
| Outcome: | The proposed benchmarks show that representation choices have measurable effects on task perfromance and that they vary depending on architecture type. |
Automatic Thesaurus Construction for Modern Hebrew (L18-1)
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| Challenge: | Modern Hebrew lacks lexical resources fundamental to many natural language processing tools. |
| Approach: | They propose a method for generating a cooccurrence based thesaurus in a MRL and a distributional similarity method for Hebrew. |
| Outcome: | The proposed method is not optimal for modern Hebrew, the authors show . they used Hebrew WordNet as their gold standard for the analysis . |
Beyond Word Boundaries: A Hebrew Coreference Benchmark and an Evaluation Protocol for Morphologically Complex Text (2026.acl-long)
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| Challenge: | CR methods originally designed for English struggle with Morphologically Rich Languages (MRLs) a single token in Hebrew may consist of multiple anaphors, and word/morpheme boundary discrepancies make mention detection and coreference resolution difficult in MRLs. |
| Approach: | They propose a CR dataset that identifies mentions at word, sub-word and multi-word levels and an evaluation protocol that directly addresses word/morpheme boundary discrepancies. |
| Outcome: | The proposed evaluation protocol directly addresses word/morpheme boundary discrepancies in Modern Hebrew, an MRL rich with complex words and pronominal clitics. |
Subword Language Model for Query Auto-Completion (D19-1)
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| Challenge: | Current neural query auto-completion systems rely on character-level language models but they slow down when queries are long. |
| Approach: | They propose to use subword language models for query auto-completion . they propose to implement a retrace algorithm and a method for approximate marginalization . |
| Outcome: | The proposed model achieves 2.5 times faster while maintaining similar quality to character-level baseline. |
LLMs Meet Isolation Kernel: Lightweight, Learning-free Binary Embeddings for Fast Retrieval (2026.findings-acl)
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| Challenge: | Large language models (LLMs) embeddings are typically high-dimensional, leading to substantial storage and retrieval overhead. |
| Approach: | They propose a learning-free method that transforms an LLM embedding into a binary embeddable using Isolation Kernel (IKE). |
| Outcome: | The proposed method performs 16.7 faster retrieval and 16 lower memory usage than the original LLM embeddings while maintaining comparable accuracy. |
MIPIC: Matryoshka Representation Learning via Self-Distilled Intra-Relational and Progressive Information Chaining (2026.findings-acl)
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Phung Gia Huy, Hai An Vu, Minh-Phuc Truong, Thang Duc Tran, Linh Ngo Van, Thanh Hong Nguyen, Trung Le
| Challenge: | Existing approaches to train dense representations require explicit coordination of how information is arranged across embedding dimensionality and model depth. |
| Approach: | They propose a framework that trains Matryoshka representations using self-distilled intra-relational alignment and Progressive information chaining. |
| Outcome: | The proposed framework produces coherent and compact Matryoshka representations with significant performance advantages under low-dimensional models. |
LEAF: Knowledge Distillation of Text Embedding Models with Teacher-Aligned Representations (2026.acl-long)
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| Challenge: | a new framework for text embedding models is available for free . asymmetrical architectures allow for flexible asymmetry, while asynchronous architectures require small batches . |
| Approach: | They propose a knowledge distillation framework for text embedding models that is compatible with their teacher . they publish leaf-ir, a 23M parameters information retrieval oriented model that ranks no.1 on BEIR . |
| Outcome: | The proposed model is compatible with teacher, enabling flexible asymmetric architectures . it sets a new state-of-the-art (SOTA) on BEIR, and achieves no.1 on the leaderboard . |