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Emilio Monti

Amazon

4 papers hereh-index 7292 citations11 works total

Matching runs newest-first, so older work may not be attached to this profile yet.

author position
  • middle author2
  • last author2

Across the 4 of 4 papers where every author was matched, so the position is known.

fields
  • cs.CL4
affiliations
  • Amazon
Homepage
same name
  • Emilio Monti — 2 papers

Either other researchers who publish under this name, or the same person where the external sources have not merged their records.

identity via Semantic Scholar / OpenAlex

activity
20172021
most citedDon't Parse, Generate! A Sequence to Sequence Architecture for Task-Oriented Semantic Parsing

23 citations · 35 across the 4 of their papers we have counts for

collaborators

4 papers

cs.CL2021

One Semantic Parser to Parse Them All: Sequence to Sequence Multi-Task Learning on Semantic Parsing Datasets

Marco Damonte, Emilio Monti

Semantic parsers map natural language utterances to meaning representations. The lack of a single standard for meaning representations led to the creation of a plethora of semantic…

cs.CL2021

Multilingual Neural Semantic Parsing for Low-Resourced Languages

Menglin Xia, Emilio Monti

Multilingual semantic parsing is a cost-effective method that allows a single model to understand different languages. However, researchers face a great imbalance of availability o…

cs.CL2020★ 23 cited

Don't Parse, Generate! A Sequence to Sequence Architecture for Task-Oriented Semantic Parsing

Subendhu Rongali, Luca Soldaini, Emilio Monti +1

Virtual assistants such as Amazon Alexa, Apple Siri, and Google Assistant often rely on a semantic parsing component to understand which action(s) to execute for an utterance spoke…

cs.CL2017★ 12 cited

Transfer Learning for Neural Semantic Parsing

Xing Fan, Emilio Monti, Lambert Mathias +1

The goal of semantic parsing is to map natural language to a machine interpretable meaning representation language (MRL). One of the constraints that limits full exploration of dee…

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Not affiliated with arXiv. Researcher data from Semantic Scholar (ODC-BY) and OpenAlex.