Search Results for author: Mohsen Mesgar

Found 19 papers, 7 papers with code

LLMs Beyond English: Scaling the Multilingual Capability of LLMs with Cross-Lingual Feedback

no code implementations3 Jun 2024 Wen Lai, Mohsen Mesgar, Alexander Fraser

We perform multilingual instruction tuning on the constructed instruction data and further align the LLMs with human feedback using the DPO algorithm on our cross-lingual human feedback dataset.

FREB-TQA: A Fine-Grained Robustness Evaluation Benchmark for Table Question Answering

2 code implementations29 Apr 2024 Wei Zhou, Mohsen Mesgar, Heike Adel, Annemarie Friedrich

To investigate these aspects, we create and publish a novel TQA evaluation benchmark in English.

Question Answering

Python Code Generation by Asking Clarification Questions

1 code implementation19 Dec 2022 Haau-Sing Li, Mohsen Mesgar, André F. T. Martins, Iryna Gurevych

We hypothesize that the under-specification of a natural language description can be resolved by asking clarification questions.

Code Generation Language Modelling

The Devil is in the Details: On Models and Training Regimes for Few-Shot Intent Classification

no code implementations12 Oct 2022 Mohsen Mesgar, Thy Thy Tran, Goran Glavas, Iryna Gurevych

First, the unexplored combination of the cross-encoder architecture (with parameterized similarity scoring function) and episodic meta-learning consistently yields the best FSIC performance.

intent-classification Intent Classification +1

ArgSciChat: A Dataset for Argumentative Dialogues on Scientific Papers

2 code implementations14 Feb 2022 Federico Ruggeri, Mohsen Mesgar, Iryna Gurevych

The applications of conversational agents for scientific disciplines (as expert domains) are understudied due to the lack of dialogue data to train such agents.

Fact Selection Response Generation

Improving Factual Consistency Between a Response and Persona Facts

no code implementations EACL 2021 Mohsen Mesgar, Edwin Simpson, Iryna Gurevych

Neural models for response generation produce responses that are semantically plausible but not necessarily factually consistent with facts describing the speaker's persona.

reinforcement-learning Reinforcement Learning (RL) +1

When is ACL's Deadline? A Scientific Conversational Agent

no code implementations23 Nov 2019 Mohsen Mesgar, Paul Youssef, Lin Li, Dominik Bierwirth, Yihao Li, Christian M. Meyer, Iryna Gurevych

Our conversational agent UKP-ATHENA assists NLP researchers in finding and exploring scientific literature, identifying relevant authors, planning or post-processing conference visits, and preparing paper submissions using a unified interface based on natural language inputs and responses.

Dialogue Coherence Assessment Without Explicit Dialogue Act Labels

1 code implementation ACL 2020 Mohsen Mesgar, Sebastian Bücker, Iryna Gurevych

Recent dialogue coherence models use the coherence features designed for monologue texts, e. g. nominal entities, to represent utterances and then explicitly augment them with dialogue-relevant features, e. g., dialogue act labels.

Multi-Task Learning

Reward Learning for Efficient Reinforcement Learning in Extractive Document Summarisation

1 code implementation30 Jul 2019 Yang Gao, Christian M. Meyer, Mohsen Mesgar, Iryna Gurevych

The predominant RL paradigm for summarisation learns a cross-input policy, which requires considerable time, data and parameter tuning due to the huge search spaces and the delayed rewards.

Decision Making Learning-To-Rank +3

Text Processing Like Humans Do: Visually Attacking and Shielding NLP Systems

1 code implementation NAACL 2019 Steffen Eger, Gözde Gül Şahin, Andreas Rücklé, Ji-Ung Lee, Claudia Schulz, Mohsen Mesgar, Krishnkant Swarnkar, Edwin Simpson, Iryna Gurevych

Visual modifications to text are often used to obfuscate offensive comments in social media (e. g., "! d10t") or as a writing style ("1337" in "leet speak"), among other scenarios.

Adversarial Attack Sentence

A Neural Local Coherence Model for Text Quality Assessment

no code implementations EMNLP 2018 Mohsen Mesgar, Michael Strube

We propose a local coherence model that captures the flow of what semantically connects adjacent sentences in a text.

Sentence Word Embeddings

Using a Graph-based Coherence Model in Document-Level Machine Translation

no code implementations WS 2017 Leo Born, Mohsen Mesgar, Michael Strube

Although coherence is an important aspect of any text generation system, it has received little attention in the context of machine translation (MT) so far.

Document Level Machine Translation Machine Translation +2

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