Ahmed Hassan

Also published as: Ahmed Hassan Awadallah


2021

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SummerTime: Text Summarization Toolkit for Non-experts
Ansong Ni | Zhangir Azerbayev | Mutethia Mutuma | Troy Feng | Yusen Zhang | Tao Yu | Ahmed Hassan Awadallah | Dragomir Radev
Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing: System Demonstrations

Recent advances in summarization provide models that can generate summaries of higher quality. Such models now exist for a number of summarization tasks, including query-based summarization, dialogue summarization, and multi-document summarization. While such models and tasks are rapidly growing in the research field, it has also become challenging for non-experts to keep track of them. To make summarization methods more accessible to a wider audience, we develop SummerTime by rethinking the summarization task from the perspective of an NLP non-expert. SummerTime is a complete toolkit for text summarization, including various models, datasets, and evaluation metrics, for a full spectrum of summarization-related tasks. SummerTime integrates with libraries designed for NLP researchers, and enables users with easy-to-use APIs. With SummerTime, users can locate pipeline solutions and search for the best model with their own data, and visualize the differences, all with a few lines of code. We also provide explanations for models and evaluation metrics to help users understand the model behaviors and select models that best suit their needs. Our library, along with a notebook demo, is available at https://github.com/Yale-LILY/SummerTime.

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A Conditional Generative Matching Model for Multi-lingual Reply Suggestion
Budhaditya Deb | Guoqing Zheng | Milad Shokouhi | Ahmed Hassan Awadallah
Findings of the Association for Computational Linguistics: EMNLP 2021

We study the problem of multilingual automated reply suggestions (RS) model serving many languages simultaneously. Multilingual models are often challenged by model capacity and severe data distribution skew across languages. While prior works largely focus on monolingual models, we propose Conditional Generative Matching models (CGM), optimized within a Variational Autoencoder framework to address challenges arising from multilingual RS. CGM does so with expressive message conditional priors, mixture densities to enhance multilingual data representation, latent alignment for language discrimination, and effective variational optimization techniques for training multilingual RS. The enhancements result in performance that exceed competitive baselines in relevance (ROUGE score) by more than 10% on average, and 16%for low resource languages. CGM also shows remarkable improvements in diversity (80%) illustrating its expressiveness in representation of multi-lingual data.

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Say ‘YES’ to Positivity: Detecting Toxic Language in Workplace Communications
Meghana Moorthy Bhat | Saghar Hosseini | Ahmed Hassan Awadallah | Paul Bennett | Weisheng Li
Findings of the Association for Computational Linguistics: EMNLP 2021

Workplace communication (e.g. email, chat, etc.) is a central part of enterprise productivity. Healthy conversations are crucial for creating an inclusive environment and maintaining harmony in an organization. Toxic communications at the workplace can negatively impact overall job satisfaction and are often subtle, hidden, or demonstrate human biases. The linguistic subtlety of mild yet hurtful conversations has made it difficult for researchers to quantify and extract toxic conversations automatically. While offensive language or hate speech has been extensively studied in social communities, there has been little work studying toxic communication in emails. Specifically, the lack of corpus, sparsity of toxicity in enterprise emails, and well-defined criteria for annotating toxic conversations have prevented researchers from addressing the problem at scale. We take the first step towards studying toxicity in workplace emails by providing (1) a general and computationally viable taxonomy to study toxic language at the workplace (2) a dataset to study toxic language at the workplace based on the taxonomy and (3) analysis on why offensive language and hate-speech datasets are not suitable to detect workplace toxicity.

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An Exploratory Study on Long Dialogue Summarization: What Works and What’s Next
Yusen Zhang | Ansong Ni | Tao Yu | Rui Zhang | Chenguang Zhu | Budhaditya Deb | Asli Celikyilmaz | Ahmed Hassan Awadallah | Dragomir Radev
Findings of the Association for Computational Linguistics: EMNLP 2021

Dialogue summarization helps readers capture salient information from long conversations in meetings, interviews, and TV series. However, real-world dialogues pose a great challenge to current summarization models, as the dialogue length typically exceeds the input limits imposed by recent transformer-based pre-trained models, and the interactive nature of dialogues makes relevant information more context-dependent and sparsely distributed than news articles. In this work, we perform a comprehensive study on long dialogue summarization by investigating three strategies to deal with the lengthy input problem and locate relevant information: (1) extended transformer models such as Longformer, (2) retrieve-then-summarize pipeline models with several dialogue utterance retrieval methods, and (3) hierarchical dialogue encoding models such as HMNet. Our experimental results on three long dialogue datasets (QMSum, MediaSum, SummScreen) show that the retrieve-then-summarize pipeline models yield the best performance. We also demonstrate that the summary quality can be further improved with a stronger retrieval model and pretraining on proper external summarization datasets.

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MetaXL: Meta Representation Transformation for Low-resource Cross-lingual Learning
Mengzhou Xia | Guoqing Zheng | Subhabrata Mukherjee | Milad Shokouhi | Graham Neubig | Ahmed Hassan Awadallah
Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

The combination of multilingual pre-trained representations and cross-lingual transfer learning is one of the most effective methods for building functional NLP systems for low-resource languages. However, for extremely low-resource languages without large-scale monolingual corpora for pre-training or sufficient annotated data for fine-tuning, transfer learning remains an understudied and challenging task. Moreover, recent work shows that multilingual representations are surprisingly disjoint across languages, bringing additional challenges for transfer onto extremely low-resource languages. In this paper, we propose MetaXL, a meta-learning based framework that learns to transform representations judiciously from auxiliary languages to a target one and brings their representation spaces closer for effective transfer. Extensive experiments on real-world low-resource languages – without access to large-scale monolingual corpora or large amounts of labeled data – for tasks like cross-lingual sentiment analysis and named entity recognition show the effectiveness of our approach. Code for MetaXL is publicly available at github.com/microsoft/MetaXL.

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Self-Training with Weak Supervision
Giannis Karamanolakis | Subhabrata Mukherjee | Guoqing Zheng | Ahmed Hassan Awadallah
Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

State-of-the-art deep neural networks require large-scale labeled training data that is often expensive to obtain or not available for many tasks. Weak supervision in the form of domain-specific rules has been shown to be useful in such settings to automatically generate weakly labeled training data. However, learning with weak rules is challenging due to their inherent heuristic and noisy nature. An additional challenge is rule coverage and overlap, where prior work on weak supervision only considers instances that are covered by weak rules, thus leaving valuable unlabeled data behind. In this work, we develop a weak supervision framework (ASTRA) that leverages all the available data for a given task. To this end, we leverage task-specific unlabeled data through self-training with a model (student) that considers contextualized representations and predicts pseudo-labels for instances that may not be covered by weak rules. We further develop a rule attention network (teacher) that learns how to aggregate student pseudo-labels with weak rule labels, conditioned on their fidelity and the underlying context of an instance. Finally, we construct a semi-supervised learning objective for end-to-end training with unlabeled data, domain-specific rules, and a small amount of labeled data. Extensive experiments on six benchmark datasets for text classification demonstrate the effectiveness of our approach with significant improvements over state-of-the-art baselines.

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Structure-Grounded Pretraining for Text-to-SQL
Xiang Deng | Ahmed Hassan Awadallah | Christopher Meek | Oleksandr Polozov | Huan Sun | Matthew Richardson
Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

Learning to capture text-table alignment is essential for tasks like text-to-SQL. A model needs to correctly recognize natural language references to columns and values and to ground them in the given database schema. In this paper, we present a novel weakly supervised Structure-Grounded pretraining framework (STRUG) for text-to-SQL that can effectively learn to capture text-table alignment based on a parallel text-table corpus. We identify a set of novel pretraining tasks: column grounding, value grounding and column-value mapping, and leverage them to pretrain a text-table encoder. Additionally, to evaluate different methods under more realistic text-table alignment settings, we create a new evaluation set Spider-Realistic based on Spider dev set with explicit mentions of column names removed, and adopt eight existing text-to-SQL datasets for cross-database evaluation. STRUG brings significant improvement over BERTLARGE in all settings. Compared with existing pretraining methods such as GRAPPA, STRUG achieves similar performance on Spider, and outperforms all baselines on more realistic sets. All the code and data used in this work will be open-sourced to facilitate future research.

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NL-EDIT: Correcting Semantic Parse Errors through Natural Language Interaction
Ahmed Elgohary | Christopher Meek | Matthew Richardson | Adam Fourney | Gonzalo Ramos | Ahmed Hassan Awadallah
Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

We study semantic parsing in an interactive setting in which users correct errors with natural language feedback. We present NL-EDIT, a model for interpreting natural language feedback in the interaction context to generate a sequence of edits that can be applied to the initial parse to correct its errors. We show that NL-EDIT can boost the accuracy of existing text-to-SQL parsers by up to 20% with only one turn of correction. We analyze the limitations of the model and discuss directions for improvement and evaluation. The code and datasets used in this paper are publicly available at http://aka.ms/NLEdit.

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QMSum: A New Benchmark for Query-based Multi-domain Meeting Summarization
Ming Zhong | Da Yin | Tao Yu | Ahmad Zaidi | Mutethia Mutuma | Rahul Jha | Ahmed Hassan Awadallah | Asli Celikyilmaz | Yang Liu | Xipeng Qiu | Dragomir Radev
Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

Meetings are a key component of human collaboration. As increasing numbers of meetings are recorded and transcribed, meeting summaries have become essential to remind those who may or may not have attended the meetings about the key decisions made and the tasks to be completed. However, it is hard to create a single short summary that covers all the content of a long meeting involving multiple people and topics. In order to satisfy the needs of different types of users, we define a new query-based multi-domain meeting summarization task, where models have to select and summarize relevant spans of meetings in response to a query, and we introduce QMSum, a new benchmark for this task. QMSum consists of 1,808 query-summary pairs over 232 meetings in multiple domains. Besides, we investigate a locate-then-summarize method and evaluate a set of strong summarization baselines on the task. Experimental results and manual analysis reveal that QMSum presents significant challenges in long meeting summarization for future research. Dataset is available at https://github.com/Yale-LILY/QMSum.

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Machine Learning-Based Approach for Arabic Dialect Identification
Hamada Nayel | Ahmed Hassan | Mahmoud Sobhi | Ahmed El-Sawy
Proceedings of the Sixth Arabic Natural Language Processing Workshop

This paper describes our systems submitted to the Second Nuanced Arabic Dialect Identification Shared Task (NADI 2021). Dialect identification is the task of automatically detecting the source variety of a given text or speech segment. There are four subtasks, two subtasks for country-level identification and the other two subtasks for province-level identification. The data in this task covers a total of 100 provinces from all 21 Arab countries and come from the Twitter domain. The proposed systems depend on five machine-learning approaches namely Complement Naïve Bayes, Support Vector Machine, Decision Tree, Logistic Regression and Random Forest Classifiers. F1 macro-averaged score of Naïve Bayes classifier outperformed all other classifiers for development and test data.

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A Dataset and Baselines for Multilingual Reply Suggestion
Mozhi Zhang | Wei Wang | Budhaditya Deb | Guoqing Zheng | Milad Shokouhi | Ahmed Hassan Awadallah
Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)

Reply suggestion models help users process emails and chats faster. Previous work only studies English reply suggestion. Instead, we present MRS, a multilingual reply suggestion dataset with ten languages. MRS can be used to compare two families of models: 1) retrieval models that select the reply from a fixed set and 2) generation models that produce the reply from scratch. Therefore, MRS complements existing cross-lingual generalization benchmarks that focus on classification and sequence labeling tasks. We build a generation model and a retrieval model as baselines for MRS. The two models have different strengths in the monolingual setting, and they require different strategies to generalize across languages. MRS is publicly available at https://github.com/zhangmozhi/mrs.

2020

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Leveraging Structured Metadata for Improving Question Answering on the Web
Xinya Du | Ahmed Hassan Awadallah | Adam Fourney | Robert Sim | Paul Bennett | Claire Cardie
Proceedings of the 1st Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 10th International Joint Conference on Natural Language Processing

We show that leveraging metadata information from web pages can improve the performance of models for answer passage selection/reranking. We propose a neural passage selection model that leverages metadata information with a fine-grained encoding strategy, which learns the representation for metadata predicates in a hierarchical way. The models are evaluated on the MS MARCO (Nguyen et al., 2016) and Recipe-MARCO datasets. Results show that our models significantly outperform baseline models, which do not incorporate metadata. We also show that the fine-grained encoding’s advantage over other strategies for encoding the metadata.

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Proceedings of the First Workshop on Natural Language Interfaces
Ahmed Hassan Awadallah | Yu Su | Huan Sun | Scott Wen-tau Yih
Proceedings of the First Workshop on Natural Language Interfaces

2019

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Multi-Source Cross-Lingual Model Transfer: Learning What to Share
Xilun Chen | Ahmed Hassan Awadallah | Hany Hassan | Wei Wang | Claire Cardie
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics

Modern NLP applications have enjoyed a great boost utilizing neural networks models. Such deep neural models, however, are not applicable to most human languages due to the lack of annotated training data for various NLP tasks. Cross-lingual transfer learning (CLTL) is a viable method for building NLP models for a low-resource target language by leveraging labeled data from other (source) languages. In this work, we focus on the multilingual transfer setting where training data in multiple source languages is leveraged to further boost target language performance. Unlike most existing methods that rely only on language-invariant features for CLTL, our approach coherently utilizes both language-invariant and language-specific features at instance level. Our model leverages adversarial networks to learn language-invariant features, and mixture-of-experts models to dynamically exploit the similarity between the target language and each individual source language. This enables our model to learn effectively what to share between various languages in the multilingual setup. Moreover, when coupled with unsupervised multilingual embeddings, our model can operate in a zero-resource setting where neither target language training data nor cross-lingual resources are available. Our model achieves significant performance gains over prior art, as shown in an extensive set of experiments over multiple text classification and sequence tagging tasks including a large-scale industry dataset.

2016

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Activity Modeling in Email
Ashequl Qadir | Michael Gamon | Patrick Pantel | Ahmed Hassan Awadallah
Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

2014

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A Random Walk–Based Model for Identifying Semantic Orientation
Ahmed Hassan | Amjad Abu-Jbara | Wanchen Lu | Dragomir Radev
Computational Linguistics, Volume 40, Issue 3 - September 2014

2013

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Identifying Web Search Query Reformulation using Concept based Matching
Ahmed Hassan
Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing

2012

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Detecting Subgroups in Online Discussions by Modeling Positive and Negative Relations among Participants
Ahmed Hassan | Amjad Abu-Jbara | Dragomir Radev
Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning

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Workshop Proceedings of TextGraphs-7: Graph-based Methods for Natural Language Processing
Irina Matveeva | Ahmed Hassan | Gael Dias
Workshop Proceedings of TextGraphs-7: Graph-based Methods for Natural Language Processing

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Extracting Signed Social Networks from Text
Ahmed Hassan | Amjad Abu-Jbara | Dragomir Radev
Workshop Proceedings of TextGraphs-7: Graph-based Methods for Natural Language Processing

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AttitudeMiner: Mining Attitude from Online Discussions
Amjad Abu-Jbara | Ahmed Hassan | Dragomir Radev
Proceedings of the Demonstration Session at the Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

2011

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Identifying the Semantic Orientation of Foreign Words
Ahmed Hassan | Amjad Abu-Jbara | Rahul Jha | Dragomir Radev
Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies

2010

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Identifying Text Polarity Using Random Walks
Ahmed Hassan | Dragomir R. Radev
Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics

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What’s with the Attitude? Identifying Sentences with Attitude in Online Discussions
Ahmed Hassan | Vahed Qazvinian | Dragomir Radev
Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing

2009

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Using Citations to Generate surveys of Scientific Paradigms
Saif Mohammad | Bonnie Dorr | Melissa Egan | Ahmed Hassan | Pradeep Muthukrishan | Vahed Qazvinian | Dragomir Radev | David Zajic
Proceedings of Human Language Technologies: The 2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics

2008

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Language Independent Text Correction using Finite State Automata
Ahmed Hassan | Sara Noeman | Hany Hassan
Proceedings of the Third International Joint Conference on Natural Language Processing: Volume-II

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Tracking the Dynamic Evolution of Participants Salience in a Discussion
Ahmed Hassan | Anthony Fader | Michael H. Crespin | Kevin M. Quinn | Burt L. Monroe | Michael Colaresi | Dragomir R. Radev
Proceedings of the 22nd International Conference on Computational Linguistics (Coling 2008)

2007

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BioNoculars: Extracting Protein-Protein Interactions from Biomedical Text
Amgad Madkour | Kareem Darwish | Hany Hassan | Ahmed Hassan | Ossama Emam
Biological, translational, and clinical language processing

2006

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Unsupervised Information Extraction Approach Using Graph Mutual Reinforcement
Hany Hassan | Ahmed Hassan | Ossama Emam
Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing

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Graph Based Semi-Supervised Approach for Information Extraction
Hany Hassan | Ahmed Hassan | Sara Noeman
Proceedings of TextGraphs: the First Workshop on Graph Based Methods for Natural Language Processing