Papers by Osmar Zaiane
OTTAWA: Optimal TransporT Adaptive Word Aligner for Hallucination and Omission Translation Errors Detection (2024.findings-acl)
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| Challenge: | Existing methods for detecting hallucinations and omissions in Machine Translation systems focus on analyzing the model’s internal states or relying on external tools. |
| Approach: | They propose an Optimal Transport-based word aligner specifically designed to enhance the detection of hallucinations and omissions in Machine Translation systems. |
| Outcome: | The proposed method is competitive with state-of-the-art methods across 18 language pairs on the HalOmi benchmark and shows promising features. |
Evaluating Coherence in Dialogue Systems using Entailment (N19-1)
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| Challenge: | Evaluating open-domain dialogue systems is difficult due to the diversity of possible correct answers. |
| Approach: | They propose a set of metrics for evaluating topic coherence using distributed sentence representations and calculable approximations of human judgment using conversational coherency. |
| Outcome: | The proposed metrics can be used as a surrogate for human judgment based on conversational coherence on large-scale datasets and provide an unbiased estimate for the quality of the responses. |
Rethinking Depression Prediction from a Fine-Grained Subscore Modeling Perspective via Multi-Task Learning (2026.acl-long)
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| Challenge: | Existing methods for depression assessment rely on standardized ratings, but they are time-consuming and subject to inter-rater variability. |
| Approach: | They propose a fine-grained model for subscore prediction via multi-task learning that can be used to predict depression severity using multiple tasks. |
| Outcome: | The proposed model outperforms baselines and Qwen3-14B direct scoring on the public E-DAIC dataset and to a large-scale private clinical dataset. |
FaithDial: A Faithful Benchmark for Information-Seeking Dialogue (2022.tacl-1)
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| Challenge: | a new benchmark for hallucination-free dialogues is based on knowledge-based conversational models that generate unsupported utterances . a recent study shows that models that are trustworthy generate unverifiable or factually incorrect statements . |
| Approach: | They propose a data-centric solution to edit hallucinated responses in the Wizard of Wikipedia benchmark. |
| Outcome: | The proposed model improves on the Wizard of Wikipedia benchmark while maintaining engaging conversations. |
Diving into the Decoding Space of Non-Autoregressive Models via Lexically Constrained Search (2026.acl-short)
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| Challenge: | Non-autoregressive (NAR) models have been mainly developed to improve decoding efficiency. |
| Approach: | They propose a search-based decoding algorithm which is comparable to the autoregressive Grid Beam Search (GBS) method. |
| Outcome: | The proposed method does not suffer from the MAP degradation issue as the autoregressive method does. |
WEXEA: Wikipedia EXhaustive Entity Annotation (2020.lrec-1)
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| Challenge: | Existing methods for extracting factual knowledge from text are limited to a few subtasks. |
| Approach: | They propose to use Wikipedia to build a corpus with exhaustive annotations of entity mentions. |
| Outcome: | The proposed system can be used to build supervised datasets and can be reproduced by everyone. |
Reusing Transferable Weight Increments for Low-resource Style Generation (2024.emnlp-main)
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| Challenge: | Text style transfer (TST) is crucial in natural language processing, aiming to endow text with a new style without altering its meaning. |
| Approach: | They propose a framework to use style features in weight increments to transfer low-resource styles effectively. |
| Outcome: | The proposed framework achieves remarkable performance across different backbones, achieving particularly effective results in low-resource scenarios. |
On the Origin of Hallucinations in Conversational Models: Is it the Datasets or the Models? (2022.naacl-main)
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| Challenge: | Existing knowledge-grounded conversational benchmarks produce factually invalid statements, a phenomenon commonly called hallucination. |
| Approach: | They conduct a human study on knowledge-grounded conversational benchmarks and state-of-the-art models. |
| Outcome: | The findings raise important questions on the quality of existing datasets and models. |
A Decoding Algorithm for Length-Control Summarization Based on Directed Acyclic Transformers (2024.findings-emnlp)
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| Challenge: | Existing methods for length control summarization treat the length requirement as a soft constraint, which may not always be satisfied. |
| Approach: | They propose a novel length-control decoding algorithm based on the directed acyclic Transformer (DAT) their approach allows for multiple plausible sequence fragments and predicts a path to connect them. |
| Outcome: | The proposed algorithm allows for multiple plausible sequence fragments and predicts a path to connect them. |