Search Results for author: Touhidul Alam

Found 3 papers, 1 papers with code

New Domain, Major Effort? How Much Data is Necessary to Adapt a Temporal Tagger to the Voice Assistant Domain

1 code implementation IWCS (ACL) 2021 Touhidul Alam, Alessandra Zarcone, Sebastian Padó

Reliable tagging of Temporal Expressions (TEs, e. g., Book a table at L’Osteria for Sunday evening) is a central requirement for Voice Assistants (VAs).

Transfer Learning

Enhancing Pipeline-Based Conversational Agents with Large Language Models

no code implementations7 Sep 2023 Mina Foosherian, Hendrik Purwins, Purna Rathnayake, Touhidul Alam, Rui Teimao, Klaus-Dieter Thoben

A hybrid approach in which LLMs' are integrated into the pipeline-based agents allows them to save time and costs of building and running agents by capitalizing on the capabilities of LLMs while retaining the integration and privacy safeguards of their existing systems.

intent-classification Intent Classification +3

PATE: A Corpus of Temporal Expressions for the In-car Voice Assistant Domain

no code implementations LREC 2020 Aless Zarcone, ra, Touhidul Alam, Zahra Kolagar

The recognition and automatic annotation of temporal expressions (e. g. {``}Add an event for tomorrow evening at eight to my calendar{''}) is a key module for AI voice assistants, in order to allow them to interact with apps (for example, a calendar app).

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