Search Results for author: Amir Hadifar

Found 7 papers, 5 papers with code

UGent-T2K at the 2nd DialDoc Shared Task: A Retrieval-Focused Dialog System Grounded in Multiple Documents

no code implementations dialdoc (ACL) 2022 Yiwei Jiang, Amir Hadifar, Johannes Deleu, Thomas Demeester, Chris Develder

Further, error analysis reveals two major failure cases, to be addressed in future work: (i) in case of topic shift within the dialog, retrieval often fails to select the correct grounding document(s), and (ii) generation sometimes fails to use the correctly retrieved grounding passage.

Passage Retrieval Response Generation +1

An Emotional Journey: Detecting Emotion Trajectories in Dutch Customer Service Dialogues

1 code implementation COLING (WNUT) 2022 Sofie Labat, Amir Hadifar, Thomas Demeester, Veronique Hoste

The ability to track fine-grained emotions in customer service dialogues has many real-world applications, but has not been studied extensively.

Learning to Reuse Distractors to support Multiple Choice Question Generation in Education

1 code implementation25 Oct 2022 Semere Kiros Bitew, Amir Hadifar, Lucas Sterckx, Johannes Deleu, Chris Develder, Thomas Demeester

This paper studies how a large existing set of manually created answers and distractors for questions over a variety of domains, subjects, and languages can be leveraged to help teachers in creating new MCQs, by the smart reuse of existing distractors.

Multiple-choice Question Generation +1

EduQG: A Multi-format Multiple Choice Dataset for the Educational Domain

no code implementations12 Oct 2022 Amir Hadifar, Semere Kiros Bitew, Johannes Deleu, Chris Develder, Thomas Demeester

Thus, our versatile dataset can be used for both question and distractor generation, as well as to explore new challenges such as question format conversion.

Distractor Generation Multiple-choice +2

A Million Tweets Are Worth a Few Points: Tuning Transformers for Customer Service Tasks

1 code implementation NAACL 2021 Amir Hadifar, Sofie Labat, Véronique Hoste, Chris Develder, Thomas Demeester

In online domain-specific customer service applications, many companies struggle to deploy advanced NLP models successfully, due to the limited availability of and noise in their datasets.

Block-wise Dynamic Sparseness

1 code implementation14 Jan 2020 Amir Hadifar, Johannes Deleu, Chris Develder, Thomas Demeester

In this paper, we present a new method for \emph{dynamic sparseness}, whereby part of the computations are omitted dynamically, based on the input.

Language Modelling

A Self-Training Approach for Short Text Clustering

1 code implementation WS 2019 Amir Hadifar, Lucas Sterckx, Thomas Demeester, Chris Develder

Short text clustering is a challenging problem when adopting traditional bag-of-words or TF-IDF representations, since these lead to sparse vector representations of the short texts.

Deep Clustering Sentence Embedding +2

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