Papers by Ignacio Iacobacci
Topic-Aware Response Generation in Task-Oriented Dialogue with Unstructured Knowledge Access (2022.findings-emnlp)
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| Challenge: | Experimental results indicate that TARG achieves state-of-the-art performance in knowledge selection and response generation, outperforming previous state- of-the art by 3.2, 3.6, and 4.2 points in EM, F1 and BLEU-4 respectively on Doc2Dial. |
| Approach: | They propose to integrate topical information into knowledge-grounded task-oriented dialogue systems by using multiple topic-aware attention mechanisms to derive the importance weighting scheme over dialogue utterances and external knowledge sources. |
| Outcome: | The proposed model outperforms existing models in knowledge selection and response generation. |
Training Dynamics for Curriculum Learning: A Study on Monolingual and Cross-lingual NLU (2022.emnlp-main)
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| Challenge: | Current approaches for NLU use CL to improve in-distribution data performance via heuristic-oriented or task-agnostic difficulties. |
| Approach: | They propose to use CL to improve in-distribution data performance by taking advantage of training dynamics as difficulty metrics instead of heuristic-oriented or task-agnostic difficulties. |
| Outcome: | The proposed model schedulers improve on in-distribution, out-of-distortion and zero-shot cross-lingual transfer datasets while being 20% faster on average. |
Conversation Graph: Data Augmentation, Training, and Evaluation for Non-Deterministic Dialogue Management (2021.tacl-1)
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| Challenge: | Existing datasets limited in size con- sidering complexity of dialogues . current trends lean towards end-to-end models while modular systems tend to be preferred in industrial applications. |
| Approach: | They propose a graph-based representation of dialogues that can be exploited for data augmentation, multi- reference training and evaluation of non-deterministic agents. |
| Outcome: | The proposed graph-based representation of dialogues can be exploited for data augmentation, multi- reference training and evaluation of non-deterministic agents. |
Compositional and Lexical Semantics in RoBERTa, BERT and DistilBERT: A Case Study on CoQA (2020.emnlp-main)
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| Challenge: | Existing knowledge transfer models do not exploit compositionality of language, often relying on superficial features. |
| Approach: | They propose to use a knowledge distillation technique to fine tune RoBERTa, BERT and DistilBERT models to improve their performance. |
| Outcome: | The proposed models improve on the CoQA task with linguistic knowledge and are able to represent compositional and lexical information. |
Multi 3 WOZ: A Multilingual, Multi-Domain, Multi-Parallel Dataset for Training and Evaluating Culturally Adapted Task-Oriented Dialog Systems (2023.tacl-1)
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Songbo Hu, Han Zhou, Mete Hergul, Milan Gritta, Guchun Zhang, Ignacio Iacobacci, Ivan Vulić, Anna Korhonen
| Challenge: | Task-oriented dialog (TOD) is one of the central objectives, hallmarks, and applications of machine intelligence. |
| Approach: | They propose a multilingual, multi-domain, multiparallele ToD dataset that offers culturally adapted dialogs in 4 languages for training and evaluation of multilingual and cross-lingual systems. |
| Outcome: | The proposed dataset is large-scale and culturally adapted to enable training and evaluation of multilingual and cross-lingual ToD systems. |
XQA-DST: Multi-Domain and Multi-Lingual Dialogue State Tracking (2023.findings-eacl)
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| Challenge: | Existing methods for capturing dialogue data are expensive and limited in their application. |
| Approach: | They propose a domain-agnostic extractive question answering approach with shared weights across domains to disentangle complex domain information in ToDs. |
| Outcome: | The proposed model can efficiently leverage domain-agnostic QA datasets while being domain-scalable and open vocabulary in DST. |
Automatic Unit Test Data Generation and Actor-Critic Reinforcement Learning for Code Synthesis (2023.findings-emnlp)
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| Challenge: | Large pre-trained language models have shown remarkable performance on various benchmarks, treating the problem of Code Generation in a fashion similar to Natural Language Generation, trained with a Language Modelling (LM) objective. |
| Approach: | They propose a method to automatically obtain function signatures and associated Unit Tests, suitable for RL training of Code Synthesis models. |
| Outcome: | The proposed model improves on a pre-trained model on various benchmarks and is more efficient than previous models. |
EntityCS: Improving Zero-Shot Cross-lingual Transfer with Entity-Centric Code Switching (2022.findings-emnlp)
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| Challenge: | Existing methods for CS use dictionaries or parallel sentences with word-alignment to generate CS data by randomly switching words in a sentence. |
| Approach: | They propose a method that focuses on Entity-level Code-Switching to capture fine-grained cross-lingual semantics without corrupting syntax. |
| Outcome: | The proposed method captures fine-grained cross-lingual semantics without corrupting syntax. |
XeroAlign: Zero-shot cross-lingual transformer alignment (2021.findings-acl)
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| Challenge: | Existing zero-shot approaches to improve multilingual NLP tasks require translation of task data to bridge the gap between the source and target language. |
| Approach: | They propose a method for task-specific alignment of cross-lingual pretrained transformers such as XeroAlign that uses translated task data to encourage the model to generate similar sentence embeddings for different languages. |
| Outcome: | The proposed method performs on par with state-of-the-art models on a cross-lingual adversarial paraphrasing task and its text classification accuracy exceeds that of XLM-R trained with labelled data. |
Improving LLM Domain Certification with Pretrained Guide Models (2026.eacl-long)
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| Challenge: | Large language models (LLMs) generate off-domain or harmful responses when deployed in high-stakes domains. |
| Approach: | They propose a method that leverages pretrained language models as guide models to sharply distinguish acceptable from refused content. |
| Outcome: | The proposed approach exploits pretrained language models as guide models while aligned to the target domain. |
Hierarchical Recurrent Aggregative Generation for Few-Shot NLG (2022.findings-acl)
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| Challenge: | Existing approaches do not account for the fact that some sub-tasks, specifically aggregation and lexicalisation, can benefit from transfer learning in different extents. |
| Approach: | They propose a hierarchical approach for few-shot and zero-shot generation using a three-moduled jointly trained architecture. |
| Outcome: | The proposed approach achieves state-of-the-art on few-shot and zero-shot settings compared to previous approaches. |
A Systematic Study of Performance Disparities in Multilingual Task-Oriented Dialogue Systems (2023.emnlp-main)
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Songbo Hu, Han Zhou, Moy Yuan, Milan Gritta, Guchun Zhang, Ignacio Iacobacci, Anna Korhonen, Ivan Vulić
| Challenge: | Existing systems trained for Arabic or Turkish using annotated data fully parallel to English ToD data still exhibit diminished ToD task performance. |
| Approach: | They define new quantitative measures of absolute and relative equivalence in system performance, capturing disparities across languages and within individual languages. |
| Outcome: | The proposed measures capture disparities across languages and within individual languages. |
CrossAligner & Co: Zero-Shot Transfer Methods for Task-Oriented Cross-lingual Natural Language Understanding (2022.findings-acl)
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| Challenge: | Task-oriented personal assistants enable people to interact with devices and services using natural language. |
| Approach: | They propose a method to acquire task knowledge in a high-resource language and then transfer it to the low-resourced language(s) they use unlabelled parallel data to perform a quantitative analysis of the methods. |
| Outcome: | The proposed methods exceed state-of-the-art (SOTA) scores across nine languages, fifteen test sets and three benchmark multilingual datasets. |
LSTMEmbed: Learning Word and Sense Representations from a Large Semantically Annotated Corpus with Long Short-Term Memories (P19-1)
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| Challenge: | Recent work has focused on vector representations which capture different meanings, i.e., senses, of words. |
| Approach: | They propose a bidirectional LSTM model which learns word senses from semantically annotated corpora by focusing on word order. |
| Outcome: | The proposed model achieves state-of-the-art on the SemEval-2014 word-to-sense similarity task and is available online at http://lcl.uniroma1.it/LSTMEmbed. |
Enhancing Transformers with Gradient Boosted Decision Trees for NLI Fine-Tuning (2021.findings-acl)
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| Challenge: | Recent advances in transfer learning have brought significant improvements to many natural language processing tasks. |
| Approach: | They propose a method of fitting a GBDT head on the features computed during finetuning to increase performance without additional computation by the neural network. |
| Outcome: | The proposed method improves on several NLI datasets using a strong baseline model (RoBERTa-large) with MNLI pretraining. |
Code-Optimise: Self-Generated Preference Data for Correctness and Efficiency (2025.findings-naacl)
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| Challenge: | Existing studies have shown that CLMs can generate accurate solutions with no regard for runtime, but at a substantial cost to correctness (down by up to 30%) |
| Approach: | They propose a framework that incorporates correctness and runtime as learning signals via self-generated preference data. |
| Outcome: | The proposed framework reduces the baseline runtimes by 6% and the average length of the generated solutions is reduced by up to 48% on MBPP and 23% on HumanEval. |
HumanRankEval: Automatic Evaluation of LMs as Conversational Assistants (2024.naacl-long)
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| Challenge: | Language models (LMs) are popular conversational assistants, but evaluation of such models is not scalable. |
| Approach: | They propose a task that performs automatic evaluation using human judgement and a large-scale set of questions with multiple answers authored and scored by humans. |
| Outcome: | The proposed task performs well with human judgements and is particularly responsive to model changes following instruction-tuning. |