Majid Yazdani


2021

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KILT: a Benchmark for Knowledge Intensive Language Tasks
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
Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

Challenging problems such as open-domain question answering, fact checking, slot filling and entity linking require access to large, external knowledge sources. While some models do well on individual tasks, developing general models is difficult as each task might require computationally expensive indexing of custom knowledge sources, in addition to dedicated infrastructure. To catalyze research on models that condition on specific information in large textual resources, we present a benchmark for knowledge-intensive language tasks (KILT). All tasks in KILT are grounded in the same snapshot of Wikipedia, reducing engineering turnaround through the re-use of components, as well as accelerating research into task-agnostic memory architectures. We test both task-specific and general baselines, evaluating downstream performance in addition to the ability of the models to provide provenance. We find that a shared dense vector index coupled with a seq2seq model is a strong baseline, outperforming more tailor-made approaches for fact checking, open-domain question answering and dialogue, and yielding competitive results on entity linking and slot filling, by generating disambiguated text. KILT data and code are available at https://github.com/facebookresearch/KILT.

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Database reasoning over text
James Thorne | Majid Yazdani | Marzieh Saeidi | Fabrizio Silvestri | Sebastian Riedel | Alon Halevy
Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)

Neural models have shown impressive performance gains in answering queries from natural language text. However, existing works are unable to support database queries, such as “List/Count all female athletes who were born in 20th century”, which require reasoning over sets of relevant facts with operations such as join, filtering and aggregation. We show that while state-of-the-art transformer models perform very well for small databases, they exhibit limitations in processing noisy data, numerical operations, and queries that aggregate facts. We propose a modular architecture to answer these database-style queries over multiple spans from text and aggregating these at scale. We evaluate the architecture using WikiNLDB, a novel dataset for exploring such queries. Our architecture scales to databases containing thousands of facts whereas contemporary models are limited by how many facts can be encoded. In direct comparison on small databases, our approach increases overall answer accuracy from 85% to 90%. On larger databases, our approach retains its accuracy whereas transformer baselines could not encode the context.

2015

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A Model of Zero-Shot Learning of Spoken Language Understanding
Majid Yazdani | James Henderson
Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing

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Learning Semantic Composition to Detect Non-compositionality of Multiword Expressions
Majid Yazdani | Meghdad Farahmand | James Henderson
Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing

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Incremental Recurrent Neural Network Dependency Parser with Search-based Discriminative Training
Majid Yazdani | James Henderson
Proceedings of the Nineteenth Conference on Computational Natural Language Learning

2014

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The PARLANCE mobile application for interactive search in English and Mandarin
Helen Hastie | Marie-Aude Aufaure | Panos Alexopoulos | Hugues Bouchard | Catherine Breslin | Heriberto Cuayáhuitl | Nina Dethlefs | Milica Gašić | James Henderson | Oliver Lemon | Xingkun Liu | Peter Mika | Nesrine Ben Mustapha | Tim Potter | Verena Rieser | Blaise Thomson | Pirros Tsiakoulis | Yves Vanrompay | Boris Villazon-Terrazas | Majid Yazdani | Steve Young | Yanchao Yu
Proceedings of the 15th Annual Meeting of the Special Interest Group on Discourse and Dialogue (SIGDIAL)

2011

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Using a Wikipedia-based Semantic Relatedness Measure for Document Clustering
Majid Yazdani | Andrei Popescu-Belis
Proceedings of TextGraphs-6: Graph-based Methods for Natural Language Processing

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A Just-in-Time Document Retrieval System for Dialogues or Monologues
Andrei Popescu-Belis | Majid Yazdani | Alexandre Nanchen | Philip N. Garner
Proceedings of the SIGDIAL 2011 Conference

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A Speech-based Just-in-Time Retrieval System using Semantic Search
Andrei Popescu-Belis | Majid Yazdani | Alexandre Nanchen | Philip N. Garner
Proceedings of the ACL-HLT 2011 System Demonstrations