Papers by Roberto Basili
Sanskrit Voyager: Unified Web Platform for Interactive Reading and Linguistic Analysis of Sanskrit Texts (2025.emnlp-demos)
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| Challenge: | Sanskrit Voyager enables users to search for words and phrases as they actually appear in texts . evaluation shows over 92% parsing accuracy on complex compounds compared to BuddhaNexus . |
| Approach: | Sanskrit Voyager is a web application for searching, reading and analyzing the Sanskrt literary corpus. |
| Outcome: | Sanskrit Voyager is a web application for searching, reading, and analyzing the Sanskrt literary corpus. |
MM-IGLU: Multi-Modal Interactive Grounded Language Understanding (2024.lrec-main)
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| Challenge: | In human-robot interaction, a robot interprets user commands related to its environment, aiming to discern whether a specific command can be executed. |
| Approach: | They propose to integrate user statements with environment's description to create a multi-modal interactive Grounded language understanding model that integrates both visual and textual data. |
| Outcome: | The proposed model integrates user’s statement with environment’s description and a cutting-edge Multi-Modal Large Language Model merges both visual and textual data. |
Learning to Generate Examples for Semantic Processing Tasks (2022.naacl-main)
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| Challenge: | Recent data augmentation techniques can help to deal with low resource settings, such as BERT, but they can hurt the results. |
| Approach: | They propose a neural approach to automatically learn to generate new examples using a pre-trained sequence-to-sequence model. |
| Outcome: | The proposed approach outperforms existing methods on text classification and natural language inference tasks by 10%. |
GAN-BERT: Generative Adversarial Learning for Robust Text Classification with a Bunch of Labeled Examples (2020.acl-main)
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| Challenge: | Recent Transformer-based architectures provide impressive results in many NLP tasks, but obtaining high-quality annotated data is expensive and time consuming. |
| Approach: | They propose a semisupervised learning method that ex- tends the fine-tuning of BERT-like architectures with unlabeled data in a generative adversarial setting. |
| Outcome: | The proposed method reduces the requirement for annotated examples while achieving good performance in sentence classification tasks. |
Learning to Solve NLP Tasks in an Incremental Number of Languages (2021.acl-short)
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| Challenge: | Existing models that support multiple languages are expensive and time-consuming to retrain . retraining only with the new language data will result in Catastrophic Forgetting of previously acquired knowledge. |
| Approach: | They propose a Continual Learning strategy that updates a model to support new languages over time while maintaining consistent results on previously learned languages. |
| Outcome: | The proposed model is based on an existing model that "teaches" to a student while the student is also trained on a new language. |
Training Multi-Modal LLMs through Dialogue Planning for HRI (2025.findings-acl)
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| Challenge: | Existing approaches to enhance Multi-Modal Large Language Models (MLLMs) with explicit dialogue planning improves response accuracy and quality, and allows models trained in one language to transfer effectively to another. |
| Approach: | They propose an approach that enhances Multi-Modal Large Language Models with a novel explicit dialogue planning phase that allows agents to refine their understanding of ambiguous commands. |
| Outcome: | The proposed approach reduces hallucinations and improves task feasibility by fine-tuning and assessing Multi-Modal models in human-robot interaction scenarios. |
Grounded Semantic Role Labelling from Synthetic Multimodal Data for Situated Robot Commands (2025.emnlp-main)
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| Challenge: | Existing symbolic parsers lack flexibility to operate in complex, dynamic environments. |
| Approach: | They propose a framework that combines frame semantics with perceptual grounding to enable robots to interpret commands via multimodal logical forms. |
| Outcome: | The proposed framework produces over 11,000 image-command pairs and lowers the cost of manual parsers. |
Auditing Deep Learning processes through Kernel-based Explanatory Models (D19-1)
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| Challenge: | Existing nonlinearity of deep learning models can be a major drawback . ethical accountability of such systems is becoming a crucial issue . |
| Approach: | They propose to use Layerwise Relevance Propagation to trace back connections between linguistic properties of input instances and system decisions. |
| Outcome: | The proposed model evaluates the transparency and coherence of analogy-based explanations modeling an audit stage for the system. |
Frame-Semantic Knowledge Injection for Event-Level Inference in LLMs (2026.acl-short)
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| Challenge: | Large language models (LLMs) are fluent but often brittle when interpretation depends on external information. |
| Approach: | They propose a framework that injects frame-semantic knowledge into Large Language Models via LoRA. |
| Outcome: | The proposed framework can generalize beyond surface cues in large language models. |