Papers by Rexhina Blloshmi
Learning When to Retrieve, What to Rewrite, and How to Respond in Conversational QA (2024.findings-emnlp)
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| Challenge: | Understanding users’ contextual search intent when generating responses is an understudied topic for conversational question answering (QA). |
| Approach: | They propose a method that allows LLMs to decide when to retrieve in RAG settings given a conversational context. |
| Outcome: | The proposed method improves on three conversational QA datasets and criticizes the quality of generated responses. |
Evaluating Multilingual Sentence Representation Models in a Real Case Scenario (2022.lrec-1)
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| Challenge: | a recent study has shown that the infamous Protocols are actually plagiarized . a convoluted task with no standard benchmarks for paraphrase detection and sentence similarity is a problem . |
| Approach: | They evaluate sentence representation models on the paraphrase detection task . they use a forged text from the so-called "Protocols of the Elders of Zion" scholars have demonstrated that the first text plagiarizes from the second . |
| Outcome: | The proposed model is based on the forged “Protocols of the Elders of Zion” . the model is similar to the standard model but has some problems . |
A Tour of Explicit Multilingual Semantics: Word Sense Disambiguation, Semantic Role Labeling and Semantic Parsing (2022.aacl-tutorials)
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| Challenge: | a recent advent of pretrained language models has sparked a revolution in NLP . but, there are still questions about whether current approaches capture explicit, symbolic meaning . this tutorial will review efforts to tackle three key open problems in lexical and sentence-level semantics . |
| Approach: | This tutorial reviews recent efforts to shed light on meaning in NLP . it will focus on three key open problems in lexical and sentence-level semantics . |
| Outcome: | This tutorial reviews recent efforts to shed light on meaning in NLP . it focuses on three key open problems in lexical and sentence-level semantics . |
Learning to Reason Over Time: Timeline Self-Reflection for Improved Temporal Reasoning in Language Models (2025.acl-long)
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| Challenge: | Large Language Models struggle with temporal reasoning, which requires processing time-related information such as event sequencing, durations, and inter-temporal relationships. |
| Approach: | They propose a framework that enhances the temporal reasoning abilities of Large Language Models (LLMs) by combining timeline construction with iterative self-reflection. |
| Outcome: | The proposed framework improves the temporal reasoning abilities of large language models and improves traceability of the inference process. |
Exploring Fine-Tuning for In-Context Retrieval and Efficient KV-Caching in Long-Context Language Models (2026.eacl-short)
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| Challenge: | Long-Context Language Models (LCLMs) can encode entire document collections, offering a strong alternative to retrieval-augmented generation (RAG). |
| Approach: | They propose to use LCLMs to encode documents with context windows of millions of tokens to improve their performance. |
| Outcome: | The proposed training strategies improve long-context performance and their robustness under compression techniques. |
IR like a SIR: Sense-enhanced Information Retrieval for Multiple Languages (2021.emnlp-main)
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Rexhina Blloshmi, Tommaso Pasini, Niccolò Campolungo, Somnath Banerjee, Roberto Navigli, Gabriella Pasi
| Challenge: | Recent advances in contextualized embeddings have made ranking on non-English documents cumbersome . a novel multilingual query expansion mechanism provides sense definitions as additional semantic information for the query. |
| Approach: | They propose a multilingual query expansion mechanism that leverages word sense information to enhance the model's performance. |
| Outcome: | The proposed model performs better than its supervised and unsupervised alternatives across languages while being trained on English Robust04 data. |
XL-AMR: Enabling Cross-Lingual AMR Parsing with Transfer Learning Techniques (2020.emnlp-main)
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| Challenge: | Abstract Meaning Representation (AMR) is a popular formalism of natural language. |
| Approach: | They develop a cross-lingual AMR parser that can be trained on the produced data . they use transfer learning techniques to produce automatic AMR annotations across languages . |
| Outcome: | The proposed parser significantly surpasses those reported in Chinese, German, Italian and Spanish. |
An Inner Table Retriever for Robust Table Question Answering (2023.acl-long)
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| Challenge: | Table Question Answering (TableQA) is a task of answering NL user questions using factoid answers extracted from table content. |
| Approach: | They propose a method for handling long tables in TableQA that extracts sub-tables to preserve the most relevant information for a question. |
| Outcome: | The proposed method can improve TableQA's accuracy with up to 1.3-4.8% and achieve state-of-the-art in two benchmarks. |
GaRAGe: A Benchmark with Grounding Annotations for RAG Evaluation (2025.findings-acl)
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| Challenge: | Large Language Models (LLMs) have shown consistent improvements across many tasks requiring natural language understanding, coding, mathematical or logical reasoning . |
| Approach: | They propose to use GaRAGe to evaluate whether LLMs can identify relevant grounding when generating RAG answers. |
| Outcome: | The proposed model over-summarises rather than ground answers strictly on annotated relevant passages, or deflects when no relevant grounding is available. |
LI-RAGE: Late Interaction Retrieval Augmented Generation with Explicit Signals for Open-Domain Table Question Answering (2023.acl-short)
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| Challenge: | Recent open-domain TableQA pipelines use a combination of retriever and reader . a table can be very large and might contain heterogeneous information across rows/columns . |
| Approach: | They propose to combine a retriever-reader pipeline with a binary relevance token to train the retriever and reader. |
| Outcome: | The proposed approaches improve on two open-domain TableQA datasets. |
SPRING Goes Online: End-to-End AMR Parsing and Generation (2021.emnlp-demo)
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| Challenge: | Abstract Meaning Representation (AMR) is a formalism for representing the semantics of natural language in a readable and hierarchical way. |
| Approach: | They present SPRING Online Services, a Web interface and RESTful APIs for their AMR parsing and generation system, SPRING (Symmetric PaRsIng aNd Generation). |
| Outcome: | The proposed system provides a highly interactive visualization platform and feedback mechanism to obtain user suggestions for further improvements of the system’s output. |
Assessing “Implicit” Retrieval Robustness of Large Language Models (2024.emnlp-main)
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| Challenge: | Retrieval-augmented generation (RAG) is a framework to enhance large language models with external knowledge, but its effectiveness is constrained by the retrieval robustness of the model. |
| Approach: | They propose to use gold and distracting context to fine-tune models to handle relevant or irrelevant retrieved context in an end-to-end manner. |
| Outcome: | The proposed model performs better when gold and distracting context are used, while still extracting correct answers when retrieval is accurate. |