Ahmed Hassan

Also published as: Ahmed Hassan Awadallah


2023

pdf bib
PREME: Preference-based Meeting Exploration through an Interactive Questionnaire
Negar Arabzadeh | Ali Ahmadvand | Julia Kiseleva | Yang Liu | Ahmed Hassan Awadallah | Ming Zhong | Milad Shokouhi
Findings of the Association for Computational Linguistics: EACL 2023

The recent increase in the volume of online meetings necessitates automated tools for organizing the material, especially when an attendee has missed the discussion and needs assistance in quickly exploring it. In this work, we propose a novel end-to-end framework for generating interactive questionnaires for preference-based meeting exploration. As a result, users are supplied with a list of suggested questions reflecting their preferences. Since the task is new, we introduce an automatic evaluation strategy by measuring how much the generated questions via questionnaire are answerable to ensure factual correctness and covers the source meeting for the depth of possible exploration.

pdf bib
Logical Transformers: Infusing Logical Structures into Pre-Trained Language Models
Borui Wang | Qiuyuan Huang | Budhaditya Deb | Aaron Halfaker | Liqun Shao | Daniel McDuff | Ahmed Hassan Awadallah | Dragomir Radev | Jianfeng Gao
Findings of the Association for Computational Linguistics: ACL 2023

Natural language contains rich logical structures and logical information, and correctly detecting and accurately understanding these logical structures and information underlying natural language texts is very crucial for NLP models’ performance on many important NLU and NLG tasks. Existing pre-trained language models based on the transformer architecture mostly adopt a classical design for constructing their input embeddings that ignores the logical structures underlying natural language texts, thus limiting their ability to better capture and encode key logical information in the input sequences. To overcome such limitations, in this paper we first propose a novel approach to construct logic-aware input embeddings for transformer language models through a combination of logic detection, logic mapping and hierarchical logical projections, and then develop a corresponding new modeling paradigm that can upgrade existing transformer language models into logical transformers to boost their performance on different NLU and NLG tasks. Our empirical experiments on four important and challenging NLU and NLG tasks demonstrate that our proposed logical transformer language models can achieve superior performance over their baseline transformer models through a deeper understanding of the logical structures of texts.

pdf bib
AutoMoE: Heterogeneous Mixture-of-Experts with Adaptive Computation for Efficient Neural Machine Translation
Ganesh Jawahar | Subhabrata Mukherjee | Xiaodong Liu | Young Jin Kim | Muhammad Abdul-Mageed | Laks Lakshmanan, V.S. | Ahmed Hassan Awadallah | Sebastien Bubeck | Jianfeng Gao
Findings of the Association for Computational Linguistics: ACL 2023

Mixture-of-Expert (MoE) models have obtained state-of-the-art performance in Neural Machine Translation (NMT) tasks. Existing works in MoE mostly consider a homogeneous design where the same number of experts of the same size are placed uniformly throughout the network. Furthermore, existing MoE works do not consider computational constraints (e.g., FLOPs, latency) to guide their design. To this end, we develop AutoMoE – a framework for designing heterogeneous MoE’s under computational constraints. AutoMoE leverages Neural Architecture Search (NAS) to obtain efficient sparse MoE sub-transformers with 4x inference speedup (CPU) and FLOPs reduction over manually designed Transformers, with parity in BLEU score over dense Transformer and within 1 BLEU point of MoE SwitchTransformer, on aggregate over benchmark datasets for NMT.Heterogeneous search space with dense and sparsely activated Transformer modules (e.g., how many experts? where to place them? what should be their sizes?) allows for adaptive compute – where different amounts of computations are used for different tokens in the input. Adaptivity comes naturally from routing decisions which send tokens to experts of different sizes. AutoMoE code, data, and trained models are available at https://aka.ms/AutoMoE.

pdf bib
Not All Counterhate Tweets Elicit the Same Replies: A Fine-Grained Analysis
Abdullah Albanyan | Ahmed Hassan | Eduardo Blanco
Proceedings of the 12th Joint Conference on Lexical and Computational Semantics (*SEM 2023)

Counterhate arguments can effectively fight and limit the spread of hate speech. However, they can also exacerbate the hate, as some people may respond with aggression if they feel threatened or targeted by the counterhate. In this paper, we investigate replies to counterhate arguments beyond whether the reply agrees or disagrees with the counterhate argument. We present a corpus with 2,621 replies to counterhate arguments countering hateful tweets, and annotate them with fine-grained characteristics. We show that (a) half of the replies (51%) to the counterhate arguments disagree with the argument, and (b) this kind of reply often supports the hateful tweet (40%). We also analyze the language of counterhate arguments that elicit certain types of replies. Experimental results show that it is feasible to anticipate the kind of replies a counterhate argument will elicit.

pdf bib
An Empirical Study of Metrics to Measure Representational Harms in Pre-Trained Language Models
Saghar Hosseini | Hamid Palangi | Ahmed Hassan Awadallah
Proceedings of the 3rd Workshop on Trustworthy Natural Language Processing (TrustNLP 2023)

Large-scale Pre-Trained Language Models (PTLMs) capture knowledge from massive human-written data which contains latent societal biases and toxic contents. In this paper, we leverage the primary task of PTLMs, i.e., language modeling, and propose a new metric to quantify manifested implicit representational harms in PTLMs towards 13 marginalized demographics. Using this metric, we conducted an empirical analysis of 24 widely used PTLMs. Our analysis provides insights into the correlation between the proposed metric in this work and other related metrics for representational harm. We observe that our metric correlates with most of the gender-specific metrics in the literature. Through extensive experiments, we explore the connections between PTLMs architectures and representational harms across two dimensions: depth and width of the networks. We found that prioritizing depth over width, mitigates representational harms in some PTLMs. Our code and data can be found at [place holder].

pdf bib
Finding Authentic Counterhate Arguments: A Case Study with Public Figures
Abdullah Albanyan | Ahmed Hassan | Eduardo Blanco
Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing

We explore authentic counterhate arguments for online hateful content toward individuals. Previous efforts are limited to counterhate to fight against hateful content toward groups. Thus, we present a corpus of 54,816 hateful tweet-paragraph pairs, where the paragraphs are candidate counterhate arguments. The counterhate arguments are retrieved from 2,500 online articles from multiple sources. We propose a methodology that assures the authenticity of the counter argument and its specificity to the individual of interest. We show that finding arguments in online articles is an efficient alternative to counterhate generation approaches that may hallucinate unsupported arguments. We also present linguistic insights on the language used in counterhate arguments. Experimental results show promising results. It is more challenging, however, to identify counterhate arguments for hateful content toward individuals not included in the training set.

pdf bib
DSEE: Dually Sparsity-embedded Efficient Tuning of Pre-trained Language Models
Xuxi Chen | Tianlong Chen | Weizhu Chen | Ahmed Hassan Awadallah | Zhangyang Wang | Yu Cheng
Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Gigantic pre-trained models have become central to natural language processing (NLP), serving as the starting point for fine-tuning towards a range of downstream tasks. However, two pain points persist for this paradigm: (a) as the pre-trained models grow bigger (e.g., 175B parameters for GPT-3), even the fine-tuning process can be time-consuming and computationally expensive; (b) the fine-tuned model has the same size as its starting point by default, which is neither sensible due to its more specialized functionality, nor practical since many fine-tuned models will be deployed in resource-constrained environments. To address these pain points, we propose a framework for resource- and parameter-efficient fine-tuning by leveraging the sparsity prior in both weight updates and the final model weights. Our proposed framework, dubbed Dually Sparsity-Embedded Efficient Tuning (DSEE), aims to achieve two key objectives: (i) parameter efficient fine-tuning - by enforcing sparsity-aware low-rank updates on top of the pre-trained weights; and (ii) resource-efficient inference - by encouraging a sparse weight structure towards the final fine-tuned model. We leverage sparsity in these two directions by exploiting both unstructured and structured sparse patterns in pre-trained language models viaa unified approach. Extensive experiments and in-depth investigations, with diverse network backbones (i.e., BERT, RoBERTa, and GPT-2) on dozens of datasets, consistently demonstrate impressive parameter-/inference-efficiency, while maintaining competitive downstream performance. For instance, DSEE saves about 25% inference FLOPs while achieving comparable performance, with 0.5% trainable parameters on BERT. Codes are available at https://github.com/VITA-Group/DSEE.

pdf bib
On Improving Summarization Factual Consistency from Natural Language Feedback
Yixin Liu | Budhaditya Deb | Milagro Teruel | Aaron Halfaker | Dragomir Radev | Ahmed Hassan Awadallah
Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Despite the recent progress in language generation models, their outputs may not always meet user expectations. In this work, we study whether informational feedback in natural language can be leveraged to improve generation quality and user preference alignment. To this end, we consider factual consistency in summarization, the quality that the summary should only contain information supported by the input documents, as the user-expected preference. We collect a high-quality dataset, DeFacto, containing human demonstrations and informational natural language feedback consisting of corrective instructions, edited summaries, and explanations with respect to the factual consistency of the summary. Using our dataset, we study three natural language generation tasks: (1) editing a summary by following the human feedback, (2) generating human feedback for editing the original summary, and (3) revising the initial summary to correct factual errors by generating both the human feedback and edited summary. We show that DeFacto can provide factually consistent human-edited summaries and further insights into summarization factual consistency thanks to its informational natural language feedback. We further demonstrate that fine-tuned language models can leverage our dataset to improve the summary factual consistency, while large language models lack the zero-shot learning ability in our proposed tasks that require controllable text generation.

pdf bib
Robustness Challenges in Model Distillation and Pruning for Natural Language Understanding
Mengnan Du | Subhabrata Mukherjee | Yu Cheng | Milad Shokouhi | Xia Hu | Ahmed Hassan Awadallah
Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics

Recent work has focused on compressing pre-trained language models (PLMs) like BERT where the major focus has been to improve the in-distribution performance for downstream tasks. However, very few of these studies have analyzed the impact of compression on the generalizability and robustness of compressed models for out-of-distribution (OOD) data. Towards this end, we study two popular model compression techniques including knowledge distillation and pruning and show that the compressed models are significantly less robust than their PLM counterparts on OOD test sets although they obtain similar performance on in-distribution development sets for a task. Further analysis indicates that the compressed models overfit on the shortcut samples and generalize poorly on the hard ones. We further leverage this observation to develop a regularization strategy for robust model compression based on sample uncertainty.

2022

pdf bib
AdaMix: Mixture-of-Adaptations for Parameter-efficient Model Tuning
Yaqing Wang | Sahaj Agarwal | Subhabrata Mukherjee | Xiaodong Liu | Jing Gao | Ahmed Hassan Awadallah | Jianfeng Gao
Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing

Standard fine-tuning of large pre-trained language models (PLMs) for downstream tasks requires updating hundreds of millions to billions of parameters, and storing a large copy of the PLM weights for every task resulting in increased cost for storing, sharing and serving the models. To address this, parameter-efficient fine-tuning (PEFT) techniques were introduced where small trainable components are injected in the PLM and updated during fine-tuning. We propose AdaMix as a general PEFT method that tunes a mixture of adaptation modules – given the underlying PEFT method of choice – introduced in each Transformer layer while keeping most of the PLM weights frozen. For instance, AdaMix can leverage a mixture of adapters like Houlsby or a mixture of low rank decomposition matrices like LoRA to improve downstream task performance over the corresponding PEFT methods for fully supervised and few-shot NLU and NLG tasks. Further, we design AdaMix such that it matches the same computational cost and the number of tunable parameters as the underlying PEFT method. By only tuning 0.1-0.2% of PLM parameters, we show that AdaMix outperforms SOTA parameter-efficient fine-tuning and full model fine-tuning for both NLU and NLG tasks.

pdf bib
Leveraging Locality in Abstractive Text Summarization
Yixin Liu | Ansong Ni | Linyong Nan | Budhaditya Deb | Chenguang Zhu | Ahmed Hassan Awadallah | Dragomir Radev
Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing

Neural attention models have achieved significant improvements on many natural language processing tasks. However, the quadratic memory complexity of the self-attention module with respect to the input length hinders their applications in long text summarization. Instead of designing more efficient attention modules, we approach this problem by investigating if models with a restricted context can have competitive performance compared with the memory-efficient attention models that maintain a global context by treating the input as a single sequence. Our model is applied to individual pages, which contain parts of inputs grouped by the principle of locality, during both the encoding and decoding stages. We empirically investigated three kinds of locality in text summarization at different levels of granularity, ranging from sentences to documents. Our experimental results show that our model has a better performance compared with strong baseline models with efficient attention modules, and our analysis provides further insights into our locality-aware modeling strategy.

pdf bib
Boosting Natural Language Generation from Instructions with Meta-Learning
Budhaditya Deb | Ahmed Hassan Awadallah | Guoqing Zheng
Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing

Recent work has shown that language models (LMs) trained with multi-task instructional learning (MTIL) can solve diverse NLP tasks in zero- and few-shot settings with improved performance compared to prompt tuning. MTIL illustrates that LMs can extract and use information about the task from instructions beyond the surface patterns of the inputs and outputs. This suggests that meta-learning may further enhance the utilization of instructions for effective task transfer. In this paper we investigate whether meta-learning applied to MTIL can further improve generalization to unseen tasks in a zero-shot setting. Specifically, we propose to adapt meta-learning to MTIL in three directions: 1) Model Agnostic Meta Learning (MAML), 2) Hyper-Network (HNet) based adaptation to generate task specific parameters conditioned on instructions, and 3) an approach combining HNet and MAML. Through extensive experiments on the large scale Natural Instructions V2 dataset, we show that our proposed approaches significantly improve over strong baselines in zero-shot settings. In particular, meta-learning improves the effectiveness of instructions and is most impactful when the test tasks are strictly zero-shot (i.e. no similar tasks in the training set) and are “hard” for LMs, illustrating the potential of meta-learning for MTIL for out-of-distribution tasks.

2021

pdf bib
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.

pdf bib
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.

pdf bib
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.

pdf bib
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.

pdf bib
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.

pdf bib
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.

pdf bib
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.

pdf bib
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.

pdf bib
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.

pdf bib
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.

pdf bib
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.

2020

pdf bib
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.

pdf bib
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

pdf bib
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

pdf bib
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

pdf bib
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

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

2012

pdf bib
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

pdf bib
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

pdf bib
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

pdf bib
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

pdf bib
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

pdf bib
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

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

2009

pdf bib
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

pdf bib
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)

pdf bib
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

2007

pdf bib
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

pdf bib
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

pdf bib
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

Search