Papers by Tommaso Pasini
A Short Survey on Sense-Annotated Corpora (2020.lrec-1)
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| Challenge: | Word Sense Disambiguation (WSD) is a key task in Natural Language Understanding. |
| Approach: | They propose to use sense-annotated corpora for supervised Word Sense Disambiguation. |
| Outcome: | The proposed methods have been compared with knowledge-based approaches and have shown to be more efficient when they are available. |
ESC: Redesigning WSD with Extractive Sense Comprehension (2021.naacl-main)
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| Challenge: | Word Sense Disambiguation (WSD) is a historical NLP task aimed at linking words in contexts to discrete sense inventories. |
| Approach: | They propose a transformer-based neural architecture for extractive Sense Comprehension to solve a span extraction problem and a new state of the art English WSD task. |
| Outcome: | The proposed model outdoes all of its competitors while relying on three times fewer annotations. |
Huge Automatically Extracted Training-Sets for Multilingual Word SenseDisambiguation (L18-1)
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| Challenge: | Word Sense Disambiguation is a crucial task in Natural Language Processing . supervised systems need to be trained on word-by-word basis, a problem that is beyond reach for resource-rich languages like English. |
| Approach: | They release six large-scale sense-annotated datasets in multiple languages to pave the way for supervised multilingual Word Sense Disambiguation. |
| Outcome: | The results show that large-scale sense annotations can be used as training sets for supervised systems. |
XL-WiC: A Multilingual Benchmark for Evaluating Semantic Contextualization (2020.emnlp-main)
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| Challenge: | Existing evaluation benchmarks for assessing distinct meanings of words are tied to sense inventories, restricting their usage to knowledge-based representation techniques. |
| Approach: | They propose a multilingual benchmark that models distinct meanings of words in English . they use a binary disambiguation task with gold standards in 12 new languages . |
| Outcome: | The proposed model can model distinct meanings of words in English even when no tagged instances are available for a target language. |
FairLex: A Multilingual Benchmark for Evaluating Fairness in Legal Text Processing (2022.acl-long)
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| Challenge: | Using pre-trained language models, we evaluate performance group disparities while none of these techniques guarantee fairness, nor consistently mitigate group disparity. |
| Approach: | They present a benchmark suite of four datasets for evaluating the fairness of pre-trained language models and the techniques used to fine-tune them for downstream tasks. |
| Outcome: | The proposed methods show that performance group disparities are vibrant in many cases, while none of these techniques guarantee fairness, nor consistently mitigate group disparity. |
With More Contexts Comes Better Performance: Contextualized Sense Embeddings for All-Round Word Sense Disambiguation (2020.emnlp-main)
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| Challenge: | Contextualized word embeddings have been used effectively across several tasks in Natural Language Processing, but it is difficult to link them to structured sources of knowledge. |
| Approach: | They propose a semi-supervised approach to producing sense embeddings for the lexical meanings within a lexicon that is comparable to that of contextualized word vectors. |
| Outcome: | The proposed approach outperforms state-of-the-art models in the English Word Sense Disambiguation task and in the multilingual one while training on sense-annotated data in English only. |
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. |
CluBERT: A Cluster-Based Approach for Learning Sense Distributions in Multiple Languages (2020.acl-main)
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| Challenge: | Existing methods to induce word senses from raw sentences lack reliable and high-coverage distributions. |
| Approach: | They propose an automatic and multilingual approach to inducing word senses from a corpus of raw sentences using an annotated corpus. |
| Outcome: | The proposed method outperforms all other methods on English and other languages. |
Sense-Annotated Corpora for Word Sense Disambiguation in Multiple Languages and Domains (2020.lrec-1)
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| Challenge: | Word Sense Disambiguation (WSD) is a field of NLP where data is usually tied to a specific language. |
| Approach: | They propose to release five large datasets annotated with word-senses in five different languages and 5 datasets in English for a different semantic domain. |
| Outcome: | The study shows that supervised models trained on the data achieve higher performance than those trained on other corpora. |
Wikipedia Entities as Rendezvous across Languages: Grounding Multilingual Language Models by Predicting Wikipedia Hyperlinks (2021.naacl-main)
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| Challenge: | Masked language models have become the de facto standard when processing text . however, these models are evaluated in a monolingual setting only . |
| Approach: | They propose a language-independent entity prediction task as an intermediate training procedure to ground word representations on entity semantics and bridge the gap between different languages. |
| Outcome: | The proposed approach bridges the gap between word representations and knowledge graphs by using a shared vocabulary of entities. |
Reducing Disambiguation Biases in NMT by Leveraging Explicit Word Sense Information (2022.naacl-main)
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| Challenge: | Recent studies show that Neural Machine Translation models struggle to disambiguate polysemous words without lapsing into their most frequent senses. |
| Approach: | They propose a way to automatically create high-precision sense-annotated parallel corpora . they then propose 'fine-tuning' strategies to exploit these sense annotations during training . |
| Outcome: | The proposed approach achieves higher BLEU scores than its vanilla counterpart in 3 language pairs. |
Just “OneSeC” for Producing Multilingual Sense-Annotated Data (P19-1)
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| Challenge: | Word Sense Disambiguation (WSD) is one of the most affected research areas . annotated data are scarce in English and almost absent in other languages . |
| Approach: | They propose a language-independent method for the automatic extraction of thousands of sentences in which a target word is tagged with its meaning. |
| Outcome: | The proposed method outperforms existing methods on multilingual and domain-specific settings. |