Mostafa Abdou


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Mapping Brains with Language Models: A Survey
Antonia Karamolegkou | Mostafa Abdou | Anders Søgaard
Findings of the Association for Computational Linguistics: ACL 2023

Over the years, many researchers have seemingly made the same observation: Brain and language model activations exhibit some structural similarities, enabling linear partial mappings between features extracted from neural recordings and computational language models. In an attempt to evaluate how much evidence has been accumulated for this observation, we survey over 30 studies spanning 10 datasets and 8 metrics. How much evidence has been accumulated, and what, if anything, is missing before we can draw conclusions? Our analysis of the evaluation methods used in the literature reveals that some of the metrics are less conservative. We also find that the accumulated evidence, for now, remains ambiguous, but correlations with model size and quality provide grounds for cautious optimism.


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Word Order Does Matter and Shuffled Language Models Know It
Mostafa Abdou | Vinit Ravishankar | Artur Kulmizev | Anders Søgaard
Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Recent studies have shown that language models pretrained and/or fine-tuned on randomly permuted sentences exhibit competitive performance on GLUE, putting into question the importance of word order information. Somewhat counter-intuitively, some of these studies also report that position embeddings appear to be crucial for models’ good performance with shuffled text. We probe these language models for word order information and investigate what position embeddings learned from shuffled text encode, showing that these models retain a notion of word order information. We show this is in part due to a subtlety in how shuffling is implemented in previous work – before rather than after subword segmentation. Surprisingly, we find even Language models trained on text shuffled after subword segmentation retain some semblance of information about word order because of the statistical dependencies between sentence length and unigram probabilities. Finally, we show that beyond GLUE, a variety of language understanding tasks do require word order information, often to an extent that cannot be learned through fine-tuning.

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Challenges and Strategies in Cross-Cultural NLP
Daniel Hershcovich | Stella Frank | Heather Lent | Miryam de Lhoneux | Mostafa Abdou | Stephanie Brandl | Emanuele Bugliarello | Laura Cabello Piqueras | Ilias Chalkidis | Ruixiang Cui | Constanza Fierro | Katerina Margatina | Phillip Rust | Anders Søgaard
Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Various efforts in the Natural Language Processing (NLP) community have been made to accommodate linguistic diversity and serve speakers of many different languages. However, it is important to acknowledge that speakers and the content they produce and require, vary not just by language, but also by culture. Although language and culture are tightly linked, there are important differences. Analogous to cross-lingual and multilingual NLP, cross-cultural and multicultural NLP considers these differences in order to better serve users of NLP systems. We propose a principled framework to frame these efforts, and survey existing and potential strategies.


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Can Language Models Encode Perceptual Structure Without Grounding? A Case Study in Color
Mostafa Abdou | Artur Kulmizev | Daniel Hershcovich | Stella Frank | Ellie Pavlick | Anders Søgaard
Proceedings of the 25th Conference on Computational Natural Language Learning

Pretrained language models have been shown to encode relational information, such as the relations between entities or concepts in knowledge-bases — (Paris, Capital, France). However, simple relations of this type can often be recovered heuristically and the extent to which models implicitly reflect topological structure that is grounded in world, such as perceptual structure, is unknown. To explore this question, we conduct a thorough case study on color. Namely, we employ a dataset of monolexemic color terms and color chips represented in CIELAB, a color space with a perceptually meaningful distance metric. Using two methods of evaluating the structural alignment of colors in this space with text-derived color term representations, we find significant correspondence. Analyzing the differences in alignment across the color spectrum, we find that warmer colors are, on average, better aligned to the perceptual color space than cooler ones, suggesting an intriguing connection to findings from recent work on efficient communication in color naming. Further analysis suggests that differences in alignment are, in part, mediated by collocationality and differences in syntactic usage, posing questions as to the relationship between color perception and usage and context.

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Do Language Models Know the Way to Rome?
Bastien Liétard | Mostafa Abdou | Anders Søgaard
Proceedings of the Fourth BlackboxNLP Workshop on Analyzing and Interpreting Neural Networks for NLP

The global geometry of language models is important for a range of applications, but language model probes tend to evaluate rather local relations, for which ground truths are easily obtained. In this paper we exploit the fact that in geography, ground truths are available beyond local relations. In a series of experiments, we evaluate the extent to which language model representations of city and country names are isomorphic to real-world geography, e.g., if you tell a language model where Paris and Berlin are, does it know the way to Rome? We find that language models generally encode limited geographic information, but with larger models performing the best, suggesting that geographic knowledge can be induced from higher-order co-occurrence statistics.

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Attention Can Reflect Syntactic Structure (If You Let It)
Vinit Ravishankar | Artur Kulmizev | Mostafa Abdou | Anders Søgaard | Joakim Nivre
Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume

Since the popularization of the Transformer as a general-purpose feature encoder for NLP, many studies have attempted to decode linguistic structure from its novel multi-head attention mechanism. However, much of such work focused almost exclusively on English — a language with rigid word order and a lack of inflectional morphology. In this study, we present decoding experiments for multilingual BERT across 18 languages in order to test the generalizability of the claim that dependency syntax is reflected in attention patterns. We show that full trees can be decoded above baseline accuracy from single attention heads, and that individual relations are often tracked by the same heads across languages. Furthermore, in an attempt to address recent debates about the status of attention as an explanatory mechanism, we experiment with fine-tuning mBERT on a supervised parsing objective while freezing different series of parameters. Interestingly, in steering the objective to learn explicit linguistic structure, we find much of the same structure represented in the resulting attention patterns, with interesting differences with respect to which parameters are frozen.


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Do Neural Language Models Show Preferences for Syntactic Formalisms?
Artur Kulmizev | Vinit Ravishankar | Mostafa Abdou | Joakim Nivre
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

Recent work on the interpretability of deep neural language models has concluded that many properties of natural language syntax are encoded in their representational spaces. However, such studies often suffer from limited scope by focusing on a single language and a single linguistic formalism. In this study, we aim to investigate the extent to which the semblance of syntactic structure captured by language models adheres to a surface-syntactic or deep syntactic style of analysis, and whether the patterns are consistent across different languages. We apply a probe for extracting directed dependency trees to BERT and ELMo models trained on 13 different languages, probing for two different syntactic annotation styles: Universal Dependencies (UD), prioritizing deep syntactic relations, and Surface-Syntactic Universal Dependencies (SUD), focusing on surface structure. We find that both models exhibit a preference for UD over SUD — with interesting variations across languages and layers — and that the strength of this preference is correlated with differences in tree shape.

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The Sensitivity of Language Models and Humans to Winograd Schema Perturbations
Mostafa Abdou | Vinit Ravishankar | Maria Barrett | Yonatan Belinkov | Desmond Elliott | Anders Søgaard
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

Large-scale pretrained language models are the major driving force behind recent improvements in perfromance on the Winograd Schema Challenge, a widely employed test of commonsense reasoning ability. We show, however, with a new diagnostic dataset, that these models are sensitive to linguistic perturbations of the Winograd examples that minimally affect human understanding. Our results highlight interesting differences between humans and language models: language models are more sensitive to number or gender alternations and synonym replacements than humans, and humans are more stable and consistent in their predictions, maintain a much higher absolute performance, and perform better on non-associative instances than associative ones.


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Better, Faster, Stronger Sequence Tagging Constituent Parsers
David Vilares | Mostafa Abdou | Anders Søgaard
Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)

Sequence tagging models for constituent parsing are faster, but less accurate than other types of parsers. In this work, we address the following weaknesses of such constituent parsers: (a) high error rates around closing brackets of long constituents, (b) large label sets, leading to sparsity, and (c) error propagation arising from greedy decoding. To effectively close brackets, we train a model that learns to switch between tagging schemes. To reduce sparsity, we decompose the label set and use multi-task learning to jointly learn to predict sublabels. Finally, we mitigate issues from greedy decoding through auxiliary losses and sentence-level fine-tuning with policy gradient. Combining these techniques, we clearly surpass the performance of sequence tagging constituent parsers on the English and Chinese Penn Treebanks, and reduce their parsing time even further. On the SPMRL datasets, we observe even greater improvements across the board, including a new state of the art on Basque, Hebrew, Polish and Swedish.

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Compositional Generalization in Image Captioning
Mitja Nikolaus | Mostafa Abdou | Matthew Lamm | Rahul Aralikatte | Desmond Elliott
Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL)

Image captioning models are usually evaluated on their ability to describe a held-out set of images, not on their ability to generalize to unseen concepts. We study the problem of compositional generalization, which measures how well a model composes unseen combinations of concepts when describing images. State-of-the-art image captioning models show poor generalization performance on this task. We propose a multi-task model to address the poor performance, that combines caption generation and image–sentence ranking, and uses a decoding mechanism that re-ranks the captions according their similarity to the image. This model is substantially better at generalizing to unseen combinations of concepts compared to state-of-the-art captioning models.

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Higher-order Comparisons of Sentence Encoder Representations
Mostafa Abdou | Artur Kulmizev | Felix Hill | Daniel M. Low | Anders Søgaard
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)

Representational Similarity Analysis (RSA) is a technique developed by neuroscientists for comparing activity patterns of different measurement modalities (e.g., fMRI, electrophysiology, behavior). As a framework, RSA has several advantages over existing approaches to interpretation of language encoders based on probing or diagnostic classification: namely, it does not require large training samples, is not prone to overfitting, and it enables a more transparent comparison between the representational geometries of different models and modalities. We demonstrate the utility of RSA by establishing a previously unknown correspondence between widely-employed pretrained language encoders and human processing difficulty via eye-tracking data, showcasing its potential in the interpretability toolbox for neural models.

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X-WikiRE: A Large, Multilingual Resource for Relation Extraction as Machine Comprehension
Mostafa Abdou | Cezar Sas | Rahul Aralikatte | Isabelle Augenstein | Anders Søgaard
Proceedings of the 2nd Workshop on Deep Learning Approaches for Low-Resource NLP (DeepLo 2019)

Although the vast majority of knowledge bases (KBs) are heavily biased towards English, Wikipedias do cover very different topics in different languages. Exploiting this, we introduce a new multilingual dataset (X-WikiRE), framing relation extraction as a multilingual machine reading problem. We show that by leveraging this resource it is possible to robustly transfer models cross-lingually and that multilingual support significantly improves (zero-shot) relation extraction, enabling the population of low-resourced KBs from their well-populated counterparts.


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AffecThor at SemEval-2018 Task 1: A cross-linguistic approach to sentiment intensity quantification in tweets
Mostafa Abdou | Artur Kulmizev | Joan Ginés i Ametllé
Proceedings of the 12th International Workshop on Semantic Evaluation

In this paper we describe our submission to SemEval-2018 Task 1: Affects in Tweets. The model which we present is an ensemble of various neural architectures and gradient boosted trees, and employs three different types of vectorial tweet representations. Furthermore, our system is language-independent and ranked first in 5 out of the 12 subtasks in which we participated, while achieving competitive results in the remaining ones. Comparatively remarkable performance is observed on both the Arabic and Spanish languages.

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Discriminator at SemEval-2018 Task 10: Minimally Supervised Discrimination
Artur Kulmizev | Mostafa Abdou | Vinit Ravishankar | Malvina Nissim
Proceedings of the 12th International Workshop on Semantic Evaluation

We participated to the SemEval-2018 shared task on capturing discriminative attributes (Task 10) with a simple system that ranked 8th amongst the 26 teams that took part in the evaluation. Our final score was 0.67, which is competitive with the winning score of 0.75, particularly given that our system is a zero-shot system that requires no training and minimal parameter optimisation. In addition to describing the submitted system, and discussing the implications of the relative success of such a system on this task, we also report on other, more complex models we experimented with.

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What can we learn from Semantic Tagging?
Mostafa Abdou | Artur Kulmizev | Vinit Ravishankar | Lasha Abzianidze | Johan Bos
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing

We investigate the effects of multi-task learning using the recently introduced task of semantic tagging. We employ semantic tagging as an auxiliary task for three different NLP tasks: part-of-speech tagging, Universal Dependency parsing, and Natural Language Inference. We compare full neural network sharing, partial neural network sharing, and what we term the learning what to share setting where negative transfer between tasks is less likely. Our findings show considerable improvements for all tasks, particularly in the learning what to share setting which shows consistent gains across all tasks.

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MGAD: Multilingual Generation of Analogy Datasets
Mostafa Abdou | Artur Kulmizev | Vinit Ravishankar
Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)


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Variable Mini-Batch Sizing and Pre-Trained Embeddings
Mostafa Abdou | Vladan Glončák | Ondřej Bojar
Proceedings of the Second Conference on Machine Translation