Papers by Vassilis Plachouras
A Comparison of Two Paraphrase Models for Taxonomy Augmentation (N18-2)
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| Challenge: | a taxonomy is often used to look up concepts in text documents. |
| Approach: | They compare two state-of-the-art paraphrase models with a paraphrase dataset . they find that paraphrasing is a viable method to augment taxonomies with more terms . |
| Outcome: | The proposed model outperforms the previous model on the risk domain. |
How Decoding Strategies Affect the Verifiability of Generated Text (2020.findings-emnlp)
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Luca Massarelli, Fabio Petroni, Aleksandra Piktus, Myle Ott, Tim Rocktäschel, Vassilis Plachouras, Fabrizio Silvestri, Sebastian Riedel
| Challenge: | Recent advances in pre-trained language models have generated text of an increasingly high quality. |
| Approach: | They propose a decoding strategy that produces less repetitive and more verifiable text. |
| Outcome: | The proposed method produces less repetitive and more verifiable text than previously used decoding strategies. |
KILT: a Benchmark for Knowledge Intensive Language Tasks (2021.naacl-main)
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Fabio Petroni, Aleksandra Piktus, Angela Fan, Patrick Lewis, Majid Yazdani, Nicola De Cao, James Thorne, Yacine Jernite, Vladimir Karpukhin, Jean Maillard, Vassilis Plachouras, Tim Rocktäschel, Sebastian Riedel
| Challenge: | Existing models for knowledge-intensive language tasks require access to large, external knowledge sources. |
| Approach: | They propose a benchmark for knowledge-intensive language tasks (KILT) they test a shared dense vector index coupled with a seq2seq model to generate disambiguated text. |
| Outcome: | The proposed model outperforms tailor-made approaches on fact checking, open-domain question answering and dialog by generating disambiguated text. |
attr2vec: Jointly Learning Word and Contextual Attribute Embeddings with Factorization Machines (N18-1)
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| Challenge: | popular word embeddings are used to learn vector representations from the context of words. |
| Approach: | They propose a framework for jointly learning embeddings for words and contextual attributes based on factorization machines. |
| Outcome: | The proposed framework improves on a text classification task compared to learning embeddings independently. |