Search Results for author: Ehsan Lotfi

Found 6 papers, 2 papers with code

Teach Me What to Say and I Will Learn What to Pick: Unsupervised Knowledge Selection Through Response Generation with Pretrained Generative Models

no code implementations EMNLP (NLP4ConvAI) 2021 Ehsan Lotfi, Maxime De Bruyn, Jeska Buhmann, Walter Daelemans

In this work we study the unsupervised selection abilities of pre-trained generative models (e. g. BART) and show that by adding a score-and-aggregate module between encoder and decoder, they are capable of learning to pick the proper knowledge through minimising the language modelling loss (i. e. without having access to knowledge labels).

Language Modelling Response Generation

ConveRT for FAQ Answering

1 code implementation2 Aug 2021 Maxime De Bruyn, Ehsan Lotfi, Jeska Buhmann, Walter Daelemans

While powerful and efficient retrieval-based models exist for English, it is rarely the case for other languages for which the same amount of training data is not available.

Chatbot

A Deep Generative Approach to Native Language Identification

no code implementations COLING 2020 Ehsan Lotfi, Ilia Markov, Walter Daelemans

Native language identification (NLI) {--} identifying the native language (L1) of a person based on his/her writing in the second language (L2) {--} is useful for a variety of purposes, including marketing, security, and educational applications.

BIG-bench Machine Learning Language Modelling +3

Sarcasm Detection Using an Ensemble Approach

no code implementations WS 2020 Jens Lemmens, Ben Burtenshaw, Ehsan Lotfi, Ilia Markov, Walter Daelemans

We present an ensemble approach for the detection of sarcasm in Reddit and Twitter responses in the context of The Second Workshop on Figurative Language Processing held in conjunction with ACL 2020.

Sarcasm Detection

Design of an Alarm System for Isfahan Ozone Level based on Artificial Intelligence Predictor Models

no code implementations8 Nov 2015 Ehsan Lotfi

The ozone level prediction is an important task of air quality agencies of modern cities.

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