Nasser Zalmout


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PV2TEA: Patching Visual Modality to Textual-Established Information Extraction
Hejie Cui | Rongmei Lin | Nasser Zalmout | Chenwei Zhang | Jingbo Shang | Carl Yang | Xian Li
Findings of the Association for Computational Linguistics: ACL 2023

Information extraction, e.g., attribute value extraction, has been extensively studied and formulated based only on text. However, many attributes can benefit from image-based extraction, like color, shape, pattern, among others. The visual modality has long been underutilized, mainly due to multimodal annotation difficulty. In this paper, we aim to patch the visual modality to the textual-established attribute in- formation extractor. The cross-modality integration faces several unique challenges: (C1) images and textual descriptions are loosely paired intra-sample and inter-samples; (C2) images usually contain rich backgrounds that can mislead the prediction; (C3) weakly supervised labels from textual-established ex- tractors are biased for multimodal training. We present PV2TEA, an encoder-decoder architecture equipped with three bias reduction schemes: (S1) Augmented label-smoothed contrast to improve the cross-modality alignment for loosely-paired image and text; (S2) Attention-pruning that adaptively distinguishes the visual foreground; (S3) Two-level neighborhood regularization that mitigates the label textual bias via reliability estimation. Empirical results on real-world e-Commerce datasets1 demonstrate up to 11.74% absolute (20.97% relatively) F1 increase over unimodal baselines.

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Generative Models for Product Attribute Extraction
Ansel Blume | Nasser Zalmout | Heng Ji | Xian Li
Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing: Industry Track

Product attribute extraction is an emerging field in information extraction and e-commerce, with applications including knowledge base construction, product recommendation, and enhancing customer experiences. In this work, we explore the use of generative models for product attribute extraction. We analyze their utility with hard and soft prompting methods, and demonstrate their ability to generate implicit attribute values, which state-of-the-art sequence tagging models are unable to extract. We perform a wide range of experiments on Amazon and MAVE product attribute datasets, and are the first to present results on multilingual attribute extraction. Our results show that generative models can outperform state- of-the-art tagging models for explicit product attribute extraction while having greater data efficiency, that they have the unique ability to perform implicit attribute extraction, and that in certain settings large language models can perform competitively with finetuned models with as little as two in-context examples.


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Ask-and-Verify: Span Candidate Generation and Verification for Attribute Value Extraction
Yifan Ding | Yan Liang | Nasser Zalmout | Xian Li | Christan Grant | Tim Weninger
Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing: Industry Track

The product attribute value extraction (AVE) task aims to capture key factual information from product profiles, and is useful for several downstream applications in e-Commerce platforms. Previous contributions usually formulate this task using sequence labeling or reading comprehension architectures. However, sequence labeling models tend to be conservative in their predictions resulting in a high false negative rate. Existing reading comprehension formulations, on the other hand, can over-generate attribute values which hinders precision. In the present work we address these limitations with a new end-to-end pipeline framework called Ask-and-Verify. Given a product and an attribute query, the Ask step detects the top-K span candidates (i.e. possible attribute values) from the product profiles, then the Verify step filters out false positive candidates. We evaluate Ask-and-Verify model on Amazon’s product pages and AliExpress public dataset, and present a comparative analysis as well as a detailed ablation study. Despite its simplicity, we show that Ask-and-Verify outperforms recent state-of-the-art models by up to 3.1% F1 absolute improvement points, while also scaling to thousands of attributes.

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Prototype-Representations for Training Data Filtering in Weakly-Supervised Information Extraction
Nasser Zalmout | Xian Li
Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing: Industry Track

The availability of high quality training data is still a bottleneck for the practical utilization of information extraction models, despite the breakthroughs in zero and few-shot learning techniques. This is further exacerbated for industry applications, where new tasks, domains, and specific use cases keep arising, which makes it impractical to depend on manually annotated data. Therefore, weak and distant supervision emerged as popular approaches to bootstrap training, utilizing labeling functions to guide the annotation process. Weakly-supervised annotation of training data is fast and efficient, however, it results in many irrelevant and out-of-context matches. This is a challenging problem that can degrade the performance in downstream models, or require a manual data cleaning step that can incur significant overhead. In this paper we present a prototype-based filtering approach, that can be utilized to denoise weakly supervised training data. The system is very simple, unsupervised, scalable, and requires little manual intervention, yet results in significant precision gains. We apply the technique in the task of attribute value extraction in e-commerce websites, and achieve up to 9% gain in precision for the downstream models, with a minimal drop in recall.


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AdaTag: Multi-Attribute Value Extraction from Product Profiles with Adaptive Decoding
Jun Yan | Nasser Zalmout | Yan Liang | Christan Grant | Xiang Ren | Xin Luna Dong
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)

Automatic extraction of product attribute values is an important enabling technology in e-Commerce platforms. This task is usually modeled using sequence labeling architectures, with several extensions to handle multi-attribute extraction. One line of previous work constructs attribute-specific models, through separate decoders or entirely separate models. However, this approach constrains knowledge sharing across different attributes. Other contributions use a single multi-attribute model, with different techniques to embed attribute information. But sharing the entire network parameters across all attributes can limit the model’s capacity to capture attribute-specific characteristics. In this paper we present AdaTag, which uses adaptive decoding to handle extraction. We parameterize the decoder with pretrained attribute embeddings, through a hypernetwork and a Mixture-of-Experts (MoE) module. This allows for separate, but semantically correlated, decoders to be generated on the fly for different attributes. This approach facilitates knowledge sharing, while maintaining the specificity of each attribute. Our experiments on a real-world e-Commerce dataset show marked improvements over previous methods.

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End-to-End Conversational Search for Online Shopping with Utterance Transfer
Liqiang Xiao | Jun Ma | Xin Luna Dong | Pascual Martínez-Gómez | Nasser Zalmout | Chenwei Zhang | Tong Zhao | Hao He | Yaohui Jin
Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing

Successful conversational search systems can present natural, adaptive and interactive shopping experience for online shopping customers. However, building such systems from scratch faces real word challenges from both imperfect product schema/knowledge and lack of training dialog data. In this work we first propose ConvSearch, an end-to-end conversational search system that deeply combines the dialog system with search. It leverages the text profile to retrieve products, which is more robust against imperfect product schema/knowledge compared with using product attributes alone. We then address the lack of data challenges by proposing an utterance transfer approach that generates dialogue utterances by using existing dialog from other domains, and leveraging the search behavior data from e-commerce retailer. With utterance transfer, we introduce a new conversational search dataset for online shopping. Experiments show that our utterance transfer method can significantly improve the availability of training dialogue data without crowd-sourcing, and the conversational search system significantly outperformed the best tested baseline.


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Morphological Analysis and Disambiguation for Gulf Arabic: The Interplay between Resources and Methods
Salam Khalifa | Nasser Zalmout | Nizar Habash
Proceedings of the Twelfth Language Resources and Evaluation Conference

In this paper we present the first full morphological analysis and disambiguation system for Gulf Arabic. We use an existing state-of-the-art morphological disambiguation system to investigate the effects of different data sizes and different combinations of morphological analyzers for Modern Standard Arabic, Egyptian Arabic, and Gulf Arabic. We find that in very low settings, morphological analyzers help boost the performance of the full morphological disambiguation task. However, as the size of resources increase, the value of the morphological analyzers decreases.

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CAMeL Tools: An Open Source Python Toolkit for Arabic Natural Language Processing
Ossama Obeid | Nasser Zalmout | Salam Khalifa | Dima Taji | Mai Oudah | Bashar Alhafni | Go Inoue | Fadhl Eryani | Alexander Erdmann | Nizar Habash
Proceedings of the Twelfth Language Resources and Evaluation Conference

We present CAMeL Tools, a collection of open-source tools for Arabic natural language processing in Python. CAMeL Tools currently provides utilities for pre-processing, morphological modeling, Dialect Identification, Named Entity Recognition and Sentiment Analysis. In this paper, we describe the design of CAMeL Tools and the functionalities it provides.

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Joint Diacritization, Lemmatization, Normalization, and Fine-Grained Morphological Tagging
Nasser Zalmout | Nizar Habash
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

The written forms of Semitic languages are both highly ambiguous and morphologically rich: a word can have multiple interpretations and is one of many inflected forms of the same concept or lemma. This is further exacerbated for dialectal content, which is more prone to noise and lacks a standard orthography. The morphological features can be lexicalized, like lemmas and diacritized forms, or non-lexicalized, like gender, number, and part-of-speech tags, among others. Joint modeling of the lexicalized and non-lexicalized features can identify more intricate morphological patterns, which provide better context modeling, and further disambiguate ambiguous lexical choices. However, the different modeling granularity can make joint modeling more difficult. Our approach models the different features jointly, whether lexicalized (on the character-level), or non-lexicalized (on the word-level). We use Arabic as a test case, and achieve state-of-the-art results for Modern Standard Arabic with 20% relative error reduction, and Egyptian Arabic with 11% relative error reduction.

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Utilizing Subword Entities in Character-Level Sequence-to-Sequence Lemmatization Models
Nasser Zalmout | Nizar Habash
Proceedings of the 28th International Conference on Computational Linguistics

In this paper we present a character-level sequence-to-sequence lemmatization model, utilizing several subword features in multiple configurations. In addition to generic n-gram embeddings (using FastText), we experiment with concatenative (stems) and templatic (roots and patterns) morphological subwords. We present several architectures that embed these features directly at the encoder side, or learn them jointly at the decoder side with a multitask learning architecture. The results indicate that using the generic n-gram embeddings (through FastText) outperform the other linguistically-driven subwords. We use Modern Standard Arabic and Egyptian Arabic as test cases, with up to 22% and 13% relative error reduction, respectively, from a strong baseline. An error analysis shows that our best system is even able to handle word/lemma pairs that are both unseen in the training data.


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Unsupervised Neologism Normalization Using Embedding Space Mapping
Nasser Zalmout | Kapil Thadani | Aasish Pappu
Proceedings of the 5th Workshop on Noisy User-generated Text (W-NUT 2019)

This paper presents an approach for detecting and normalizing neologisms in social media content. Neologisms refer to recent expressions that are specific to certain entities or events and are being increasingly used by the public, but have not yet been accepted in mainstream language. Automated methods for handling neologisms are important for natural language understanding and normalization, especially for informal genres with user generated content. We present an unsupervised approach for detecting neologisms and then normalizing them to canonical words without relying on parallel training data. Our approach builds on the text normalization literature and introduces adaptations to fit the specificities of this task, including phonetic and etymological considerations. We evaluate the proposed techniques on a dataset of Reddit comments, with detected neologisms and corresponding normalizations.

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Adversarial Multitask Learning for Joint Multi-Feature and Multi-Dialect Morphological Modeling
Nasser Zalmout | Nizar Habash
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics

Morphological tagging is challenging for morphologically rich languages due to the large target space and the need for more training data to minimize model sparsity. Dialectal variants of morphologically rich languages suffer more as they tend to be more noisy and have less resources. In this paper we explore the use of multitask learning and adversarial training to address morphological richness and dialectal variations in the context of full morphological tagging. We use multitask learning for joint morphological modeling for the features within two dialects, and as a knowledge-transfer scheme for cross-dialectal modeling. We use adversarial training to learn dialect invariant features that can help the knowledge-transfer scheme from the high to low-resource variants. We work with two dialectal variants: Modern Standard Arabic (high-resource “dialect’”) and Egyptian Arabic (low-resource dialect) as a case study. Our models achieve state-of-the-art results for both. Furthermore, adversarial training provides more significant improvement when using smaller training datasets in particular.


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Unified Guidelines and Resources for Arabic Dialect Orthography
Nizar Habash | Fadhl Eryani | Salam Khalifa | Owen Rambow | Dana Abdulrahim | Alexander Erdmann | Reem Faraj | Wajdi Zaghouani | Houda Bouamor | Nasser Zalmout | Sara Hassan | Faisal Al-Shargi | Sakhar Alkhereyf | Basma Abdulkareem | Ramy Eskander | Mohammad Salameh | Hind Saddiki
Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)

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Addressing Noise in Multidialectal Word Embeddings
Alexander Erdmann | Nasser Zalmout | Nizar Habash
Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)

Word embeddings are crucial to many natural language processing tasks. The quality of embeddings relies on large non-noisy corpora. Arabic dialects lack large corpora and are noisy, being linguistically disparate with no standardized spelling. We make three contributions to address this noise. First, we describe simple but effective adaptations to word embedding tools to maximize the informative content leveraged in each training sentence. Second, we analyze methods for representing disparate dialects in one embedding space, either by mapping individual dialects into a shared space or learning a joint model of all dialects. Finally, we evaluate via dictionary induction, showing that two metrics not typically reported in the task enable us to analyze our contributions’ effects on low and high frequency words. In addition to boosting performance between 2-53%, we specifically improve on noisy, low frequency forms without compromising accuracy on high frequency forms.

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Noise-Robust Morphological Disambiguation for Dialectal Arabic
Nasser Zalmout | Alexander Erdmann | Nizar Habash
Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)

User-generated text tends to be noisy with many lexical and orthographic inconsistencies, making natural language processing (NLP) tasks more challenging. The challenging nature of noisy text processing is exacerbated for dialectal content, where in addition to spelling and lexical differences, dialectal text is characterized with morpho-syntactic and phonetic variations. These issues increase sparsity in NLP models and reduce accuracy. We present a neural morphological tagging and disambiguation model for Egyptian Arabic, with various extensions to handle noisy and inconsistent content. Our models achieve about 5% relative error reduction (1.1% absolute improvement) for full morphological analysis, and around 22% relative error reduction (1.8% absolute improvement) for part-of-speech tagging, over a state-of-the-art baseline.

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Utilizing Character and Word Embeddings for Text Normalization with Sequence-to-Sequence Models
Daniel Watson | Nasser Zalmout | Nizar Habash
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing

Text normalization is an important enabling technology for several NLP tasks. Recently, neural-network-based approaches have outperformed well-established models in this task. However, in languages other than English, there has been little exploration in this direction. Both the scarcity of annotated data and the complexity of the language increase the difficulty of the problem. To address these challenges, we use a sequence-to-sequence model with character-based attention, which in addition to its self-learned character embeddings, uses word embeddings pre-trained with an approach that also models subword information. This provides the neural model with access to more linguistic information especially suitable for text normalization, without large parallel corpora. We show that providing the model with word-level features bridges the gap for the neural network approach to achieve a state-of-the-art F1 score on a standard Arabic language correction shared task dataset.


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A Parallel Corpus for Evaluating Machine Translation between Arabic and European Languages
Nizar Habash | Nasser Zalmout | Dima Taji | Hieu Hoang | Maverick Alzate
Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 2, Short Papers

We present Arab-Acquis, a large publicly available dataset for evaluating machine translation between 22 European languages and Arabic. Arab-Acquis consists of over 12,000 sentences from the JRC-Acquis (Acquis Communautaire) corpus translated twice by professional translators, once from English and once from French, and totaling over 600,000 words. The corpus follows previous data splits in the literature for tuning, development, and testing. We describe the corpus and how it was created. We also present the first benchmarking results on translating to and from Arabic for 22 European languages.

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Don’t Throw Those Morphological Analyzers Away Just Yet: Neural Morphological Disambiguation for Arabic
Nasser Zalmout | Nizar Habash
Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing

This paper presents a model for Arabic morphological disambiguation based on Recurrent Neural Networks (RNN). We train Long Short-Term Memory (LSTM) cells in several configurations and embedding levels to model the various morphological features. Our experiments show that these models outperform state-of-the-art systems without explicit use of feature engineering. However, adding learning features from a morphological analyzer to model the space of possible analyses provides additional improvement. We make use of the resulting morphological models for scoring and ranking the analyses of the morphological analyzer for morphological disambiguation. The results show significant gains in accuracy across several evaluation metrics. Our system results in 4.4% absolute increase over the state-of-the-art in full morphological analysis accuracy (30.6% relative error reduction), and 10.6% (31.5% relative error reduction) for out-of-vocabulary words.


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YAMAMA: Yet Another Multi-Dialect Arabic Morphological Analyzer
Salam Khalifa | Nasser Zalmout | Nizar Habash
Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: System Demonstrations

In this paper, we present YAMAMA, a multi-dialect Arabic morphological analyzer and disambiguator. Our system is almost five times faster than the state-of-art MADAMIRA system with a slightly lower quality. In addition to speed, YAMAMA outputs a rich representation which allows for a wider spectrum of use. In this regard, YAMAMA transcends other systems, such as FARASA, which is faster but provides specific outputs catering to specific applications.

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Analysis of Foreign Language Teaching Methods: An Automatic Readability Approach
Nasser Zalmout | Hind Saddiki | Nizar Habash
Proceedings of the 3rd Workshop on Natural Language Processing Techniques for Educational Applications (NLPTEA2016)

Much research in education has been done on the study of different language teaching methods. However, there has been little investigation using computational analysis to compare such methods in terms of readability or complexity progression. In this paper, we make use of existing readability scoring techniques and our own classifiers to analyze the textbooks used in two very different teaching methods for English as a Second Language – the grammar-based and the communicative methods. Our analysis indicates that the grammar-based curriculum shows a more coherent readability progression compared to the communicative curriculum. This finding corroborates with the expectations about the differences between these two methods and validates our approach’s value in comparing different teaching methods quantitatively.


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Building a Corpus for Palestinian Arabic: a Preliminary Study
Mustafa Jarrar | Nizar Habash | Diyam Akra | Nasser Zalmout
Proceedings of the EMNLP 2014 Workshop on Arabic Natural Language Processing (ANLP)