Search Results for author: Hamed Firooz

Found 15 papers, 5 papers with code

Modality-specific Distillation

no code implementations NAACL (maiworkshop) 2021 Woojeong Jin, Maziar Sanjabi, Shaoliang Nie, Liang Tan, Xiang Ren, Hamed Firooz

In this paper, we propose modality-specific distillation (MSD) to effectively transfer knowledge from a teacher on multimodal datasets.

Knowledge Distillation Meta-Learning

ER-TEST: Evaluating Explanation Regularization Methods for NLP Models

no code implementations25 May 2022 Brihi Joshi, Aaron Chan, Ziyi Liu, Shaoliang Nie, Maziar Sanjabi, Hamed Firooz, Xiang Ren

Plus, little is understood about how ER model performance is affected by the choice of ER criteria or by the number/choice of training instances with human rationales.

Detecting and Understanding Harmful Memes: A Survey

1 code implementation9 May 2022 Shivam Sharma, Firoj Alam, Md. Shad Akhtar, Dimitar Dimitrov, Giovanni Da San Martino, Hamed Firooz, Alon Halevy, Fabrizio Silvestri, Preslav Nakov, Tanmoy Chakraborty

One interesting finding is that many types of harmful memes are not really studied, e. g., such featuring self-harm and extremism, partly due to the lack of suitable datasets.

Detection, Disambiguation, Re-ranking: Autoregressive Entity Linking as a Multi-Task Problem

no code implementations Findings (ACL) 2022 Khalil Mrini, Shaoliang Nie, Jiatao Gu, Sinong Wang, Maziar Sanjabi, Hamed Firooz

Without the use of a knowledge base or candidate sets, our model sets a new state of the art in two benchmark datasets of entity linking: COMETA in the biomedical domain, and AIDA-CoNLL in the news domain.

Entity Linking Re-Ranking

Understanding Failure Modes of Self-Supervised Learning

no code implementations3 Mar 2022 Neha Mukund Kalibhat, Kanika Narang, Liang Tan, Hamed Firooz, Maziar Sanjabi, Soheil Feizi

In this paper, we tackle these issues and study the representation space of self-supervised models by understanding the underlying reasons for misclassifications in a downstream task.

Self-Supervised Learning

BARACK: Partially Supervised Group Robustness With Guarantees

no code implementations31 Dec 2021 Nimit S. Sohoni, Maziar Sanjabi, Nicolas Ballas, Aditya Grover, Shaoliang Nie, Hamed Firooz, Christopher Ré

Theoretically, we provide generalization bounds for our approach in terms of the worst-group performance, which scale with respect to both the total number of training points and the number of training points with group labels.

Fairness Generalization Bounds

A Fistful of Words: Learning Transferable Visual Models from Bag-of-Words Supervision

no code implementations27 Dec 2021 Ajinkya Tejankar, Maziar Sanjabi, Bichen Wu, Saining Xie, Madian Khabsa, Hamed Pirsiavash, Hamed Firooz

In this paper, we focus on teasing out what parts of the language supervision are essential for training zero-shot image classification models.

Classification Image Captioning +3

UNIREX: A Unified Learning Framework for Language Model Rationale Extraction

no code implementations BigScience (ACL) 2022 Aaron Chan, Maziar Sanjabi, Lambert Mathias, Liang Tan, Shaoliang Nie, Xiaochang Peng, Xiang Ren, Hamed Firooz

An extractive rationale explains a language model's (LM's) prediction on a given task instance by highlighting the text inputs that most influenced the prediction.

Language Modelling Text Classification

Detecting Propaganda Techniques in Memes

1 code implementation ACL 2021 Dimitar Dimitrov, Bishr Bin Ali, Shaden Shaar, Firoj Alam, Fabrizio Silvestri, Hamed Firooz, Preslav Nakov, Giovanni Da San Martino

We further create and release a new corpus of 950 memes, carefully annotated with 22 propaganda techniques, which can appear in the text, in the image, or in both.

SemEval-2021 Task 6: Detection of Persuasion Techniques in Texts and Images

1 code implementation SEMEVAL 2021 Dimitar Dimitrov, Bishr Bin Ali, Shaden Shaar, Firoj Alam, Fabrizio Silvestri, Hamed Firooz, Preslav Nakov, Giovanni Da San Martino

We describe SemEval-2021 task 6 on Detection of Persuasion Techniques in Texts and Images: the data, the annotation guidelines, the evaluation setup, the results, and the participating systems.

Adversarial Evaluation of Multimodal Models under Realistic Gray Box Assumption

no code implementations25 Nov 2020 Ivan Evtimov, Russel Howes, Brian Dolhansky, Hamed Firooz, Cristian Canton Ferrer

This work examines the vulnerability of multimodal (image + text) models to adversarial threats similar to those discussed in previous literature on unimodal (image- or text-only) models.

General Classification Text Augmentation

Supervised Multimodal Bitransformers for Classifying Images and Text

6 code implementations6 Sep 2019 Douwe Kiela, Suvrat Bhooshan, Hamed Firooz, Ethan Perez, Davide Testuggine

Self-supervised bidirectional transformer models such as BERT have led to dramatic improvements in a wide variety of textual classification tasks.

Classification General Classification

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