Author name cluster

Vittorio Perera

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

3 papers

2 author rows

AAAI Conference 2018 Conference Paper

Multi-Task Learning For Parsing The Alexa Meaning Representation Language

Vittorio Perera
Tagyoung Chung
Thomas Kollar
Emma Strubell

The Alexa Meaning Representation Language (AMRL) is a compositional graph-based semantic representation that includes ﬁne-grained types, properties, actions, and roles and can represent a wide variety of spoken language. AMRL increases the ability of virtual assistants to represent more complex requests, including logical and conditional statements as well as ones with nested clauses. Due to this representational capacity, the acquisition of large scale data resources is challenging, which limits the accuracy of resulting models. This paper has two primary contributions. The ﬁrst contribution is a linearization of the AMRL parses that aligns it to a related task of spoken language understanding (SLU) and a deep neural network architecture that uses multi-task learning to predict AMRL ﬁne-grained types, properties and intents. The second contribution is a deep neural network architecture that leverages embeddings from the large-scale data resources that are available for SLU. When combined, these contributions enable the training of accurate models of AMRL parsing, even in the presence of data sparsity. The proposed models, which use the linearized AMRL parse, multi-task learning, residual connections and embeddings from SLU, decrease the error rates in the prediction of the full AMRL parse by 3. 56% absolute.

PDF Details

IJCAI Conference 2015 Conference Paper

Handling Complex Commands as Service Robot Task Requests

Vittorio Perera
Manuela Veloso

We contribute a novel approach to understand, dialogue, plan, and execute complex sentences to command a mobile service robot. We define a complex command as a natural language sentence consisting of sensing-based conditionals, conjunctions, and disjunctions. We introduce a flexible templatebased algorithm to extract such structure from the parse tree of the sentence. As the complexity of the command increases, extracting the right structure using the template-based algorithm decreases becomes more problematic. We introduce two different dialogue approaches that enable the user to confirm or correct the extracted command structure. We present how the structure used to represent complex commands can be directly used for planning and execution by the service robot. We show results on a corpus of 100 complex commands.

PDF Details

ICRA Conference 2013 Conference Paper

Learning environmental knowledge from task-based human-robot dialog

Thomas Kollar
Vittorio Perera
Daniele Nardi
Manuela Veloso

This paper presents an approach for learning environmental knowledge from task-based human-robot dialog. Previous approaches to dialog use domain knowledge to constrain the types of language people are likely to use. In contrast, by introducing a joint probabilistic model over speech, the resulting semantic parse and the mapping from each element of the parse to a physical entity in the building (e. g. , grounding), our approach is flexible to the ways that untrained people interact with robots, is robust to speech to text errors and is able to learn referring expressions for physical locations in a map (e. g. , to create a semantic map). Our approach has been evaluated by having untrained people interact with a service robot. Starting with an empty semantic map, our approach is able ask 50% fewer questions than a baseline approach, thereby enabling more effective and intuitive human robot dialog.

Details