Asad Sayeed

Also published as: Asad B. Sayeed


Thematic Fit Bits: Annotation Quality and Quantity Interplay for Event Participant Representation
Yuval Marton | Asad Sayeed
Proceedings of the Thirteenth Language Resources and Evaluation Conference

Modeling thematic fit (a verb-argument compositional semantics task) currently requires a very large burden of labeled data. We take a linguistically machine-annotated large corpus and replace corpus layers with output from higher-quality, more modern taggers. We compare the old and new corpus versions’ impact on a verb-argument fit modeling task, using a high-performing neural approach. We discover that higher annotation quality dramatically reduces our data requirement while demonstrating better supervised predicate-argument classification. But in applying the model to psycholinguistic tasks outside the training objective, we see clear gains at scale, but only in one of two thematic fit estimation tasks, and no clear gains on the other. We also see that quality improves with training size, but perhaps plateauing or even declining in one task. Last, we tested the effect of role set size. All this suggests that the quality/quantity interplay is not all you need. We replicate previous studies while modifying certain role representation details and set a new state-of-the-art in event modeling, using a fraction of the data. We make the new corpus version public.

Where’s the Learning in Representation Learning for Compositional Semantics and the Case of Thematic Fit
Mughilan Muthupari | Samrat Halder | Asad Sayeed | Yuval Marton
Proceedings of the Fifth BlackboxNLP Workshop on Analyzing and Interpreting Neural Networks for NLP

Observing that for certain NLP tasks, such as semantic role prediction or thematic fit estimation, random embeddings perform as well as pre-trained embeddings, we explore what settings allow for this, and examine where most of the learning is encoded: the word embeddings, the semantic role embeddings, or “the network”. We find nuanced answers, depending on the task and its relation to the training objective. We examine these representation learning aspects in multi-task learning, where role prediction and role-filling are supervised tasks, while several thematic fit tasks are outside the models’ direct supervision. We observe a non-monotonous relation between some tasks’ quality scores and the training data size. In order to better understand this observation, we analyze these results using easier, per-verb versions of these tasks.

Distributional properties of political dogwhistle representations in Swedish BERT
Niclas Hertzberg | Robin Cooper | Elina Lindgren | Björn Rönnerstrand | Gregor Rettenegger | Ellen Breitholtz | Asad Sayeed
Proceedings of the Sixth Workshop on Online Abuse and Harms (WOAH)

“Dogwhistles” are expressions intended by the speaker have two messages: a socially-unacceptable “in-group” message understood by a subset of listeners, and a benign message intended for the out-group. We take the result of a word-replacement survey of the Swedish population intended to reveal how dogwhistles are understood, and we show that the difficulty of annotating dogwhistles is reflected in the separability in the space of a sentence-transformer Swedish BERT trained on general data.

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Proceedings of the 2022 CLASP Conference on (Dis)embodiment
Simon Dobnik | Julian Grove | Asad Sayeed
Proceedings of the 2022 CLASP Conference on (Dis)embodiment


Semantic shift in social networks
Bill Noble | Asad Sayeed | Raquel Fernández | Staffan Larsson
Proceedings of *SEM 2021: The Tenth Joint Conference on Lexical and Computational Semantics

Just as the meaning of words is tied to the communities in which they are used, so too is semantic change. But how does lexical semantic change manifest differently across different communities? In this work, we investigate the relationship between community structure and semantic change in 45 communities from the social media website Reddit. We use distributional methods to quantify lexical semantic change and induce a social network on communities, based on interactions between members. We explore the relationship between semantic change and the clustering coefficient of a community’s social network graph, as well as community size and stability. While none of these factors are found to be significant on their own, we report a significant effect of their three-way interaction. We also report on significant word-level effects of frequency and change in frequency, which replicate previous findings.


Building Sense Representations in Danish by Combining Word Embeddings with Lexical Resources
Ida Rørmann Olsen | Bolette Pedersen | Asad Sayeed
Proceedings of the 2020 Globalex Workshop on Linked Lexicography

Our aim is to identify suitable sense representations for NLP in Danish. We investigate sense inventories that correlate with human interpretations of word meaning and ambiguity as typically described in dictionaries and wordnets and that are well reflected distributionally as expressed in word embeddings. To this end, we study a number of highly ambiguous Danish nouns and examine the effectiveness of sense representations constructed by combining vectors from a distributional model with the information from a wordnet. We establish representations based on centroids obtained from wordnet synests and example sentences as well as representations established via are tested in a word sense disambiguation task. We conclude that the more information extracted from the wordnet entries (example sentence, definition, semantic relations) the more successful the sense representation vector.

Diverse and Relevant Visual Storytelling with Scene Graph Embeddings
Xudong Hong | Rakshith Shetty | Asad Sayeed | Khushboo Mehra | Vera Demberg | Bernt Schiele
Proceedings of the 24th Conference on Computational Natural Language Learning

A problem in automatically generated stories for image sequences is that they use overly generic vocabulary and phrase structure and fail to match the distributional characteristics of human-generated text. We address this problem by introducing explicit representations for objects and their relations by extracting scene graphs from the images. Utilizing an embedding of this scene graph enables our model to more explicitly reason over objects and their relations during story generation, compared to the global features from an object classifier used in previous work. We apply metrics that account for the diversity of words and phrases of generated stories as well as for reference to narratively-salient image features and show that our approach outperforms previous systems. Our experiments also indicate that our models obtain competitive results on reference-based metrics.

Exploiting Cross-Lingual Hints to Discover Event Pronouns
Sharid Loáiciga | Christian Hardmeier | Asad Sayeed
Proceedings of the Twelfth Language Resources and Evaluation Conference

Non-nominal co-reference is much less studied than nominal coreference, partly because of the lack of annotated corpora. We explore the possibility to exploit parallel multilingual corpora as a means of cheap supervision for the classification of three different readings of the English pronoun ‘it’: entity, event or pleonastic, from their translation in several languages. We found that the ‘event’ reading is not very frequent, but can be easily predicted provided that the construction used to translate the ‘it’ example is a pronoun as well. These cases, nevertheless, are not enough to generalize to other types of non-nominal reference.

An Annotation Approach for Social and Referential Gaze in Dialogue
Vidya Somashekarappa | Christine Howes | Asad Sayeed
Proceedings of the Twelfth Language Resources and Evaluation Conference

This paper introduces an approach for annotating eye gaze considering both its social and the referential functions in multi-modal human-human dialogue. Detecting and interpreting the temporal patterns of gaze behavior cues is natural for humans and also mostly an unconscious process. However, these cues are difficult for conversational agents such as robots or avatars to process or generate. The key factor is to recognize these variants and carry out a successful conversation, as misinterpretation can lead to total failure of the given interaction. This paper introduces an annotation scheme for eye-gaze in human-human dyadic interactions that is intended to facilitate the learning of eye-gaze patterns in multi-modal natural dialogue.


Verb-Second Effect on Quantifier Scope Interpretation
Asad Sayeed | Matthias Lindemann | Vera Demberg
Proceedings of the Workshop on Cognitive Modeling and Computational Linguistics

Sentences like “Every child climbed a tree” have at least two interpretations depending on the precedence order of the universal quantifier and the indefinite. Previous experimental work explores the role that different mechanisms such as semantic reanalysis and world knowledge may have in enabling each interpretation. This paper discusses a web-based task that uses the verb-second characteristic of German main clauses to estimate the influence of word order variation over world knowledge.

A Hybrid Model for Globally Coherent Story Generation
Fangzhou Zhai | Vera Demberg | Pavel Shkadzko | Wei Shi | Asad Sayeed
Proceedings of the Second Workshop on Storytelling

Automatically generating globally coherent stories is a challenging problem. Neural text generation models have been shown to perform well at generating fluent sentences from data, but they usually fail to keep track of the overall coherence of the story after a couple of sentences. Existing work that incorporates a text planning module succeeded in generating recipes and dialogues, but appears quite data-demanding. We propose a novel story generation approach that generates globally coherent stories from a fairly small corpus. The model exploits a symbolic text planning module to produce text plans, thus reducing the demand of data; a neural surface realization module then generates fluent text conditioned on the text plan. Human evaluation showed that our model outperforms various baselines by a wide margin and generates stories which are fluent as well as globally coherent.


Rollenwechsel-English: a large-scale semantic role corpus
Asad Sayeed | Pavel Shkadzko | Vera Demberg
Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)

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Learning distributed event representations with a multi-task approach
Xudong Hong | Asad Sayeed | Vera Demberg
Proceedings of the Seventh Joint Conference on Lexical and Computational Semantics

Human world knowledge contains information about prototypical events and their participants and locations. In this paper, we train the first models using multi-task learning that can both predict missing event participants and also perform semantic role classification based on semantic plausibility. Our best-performing model is an improvement over the previous state-of-the-art on thematic fit modelling tasks. The event embeddings learned by the model can additionally be used effectively in an event similarity task, also outperforming the state-of-the-art.

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Proceedings of the 8th Workshop on Cognitive Modeling and Computational Linguistics (CMCL 2018)
Asad Sayeed | Cassandra Jacobs | Tal Linzen | Marten van Schijndel
Proceedings of the 8th Workshop on Cognitive Modeling and Computational Linguistics (CMCL 2018)


Modeling Semantic Expectation: Using Script Knowledge for Referent Prediction
Ashutosh Modi | Ivan Titov | Vera Demberg | Asad Sayeed | Manfred Pinkal
Transactions of the Association for Computational Linguistics, Volume 5

Recent research in psycholinguistics has provided increasing evidence that humans predict upcoming content. Prediction also affects perception and might be a key to robustness in human language processing. In this paper, we investigate the factors that affect human prediction by building a computational model that can predict upcoming discourse referents based on linguistic knowledge alone vs. linguistic knowledge jointly with common-sense knowledge in the form of scripts. We find that script knowledge significantly improves model estimates of human predictions. In a second study, we test the highly controversial hypothesis that predictability influences referring expression type but do not find evidence for such an effect.

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Proceedings of the 7th Workshop on Cognitive Modeling and Computational Linguistics (CMCL 2017)
Ted Gibson | Tal Linzen | Asad Sayeed | Martin van Schijndel | William Schuler
Proceedings of the 7th Workshop on Cognitive Modeling and Computational Linguistics (CMCL 2017)


Event participant modelling with neural networks
Ottokar Tilk | Vera Demberg | Asad Sayeed | Dietrich Klakow | Stefan Thater
Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing

Roleo: Visualising Thematic Fit Spaces on the Web
Asad Sayeed | Xudong Hong | Vera Demberg
Proceedings of ACL-2016 System Demonstrations

LingoTurk: managing crowdsourced tasks for psycholinguistics
Florian Pusse | Asad Sayeed | Vera Demberg
Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Demonstrations

Thematic fit evaluation: an aspect of selectional preferences
Asad Sayeed | Clayton Greenberg | Vera Demberg
Proceedings of the 1st Workshop on Evaluating Vector-Space Representations for NLP


Improving unsupervised vector-space thematic fit evaluation via role-filler prototype clustering
Clayton Greenberg | Asad Sayeed | Vera Demberg
Proceedings of the 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

Vector-space calculation of semantic surprisal for predicting word pronunciation duration
Asad Sayeed | Stefan Fischer | Vera Demberg
Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)

Verb polysemy and frequency effects in thematic fit modeling
Clayton Greenberg | Vera Demberg | Asad Sayeed
Proceedings of the 6th Workshop on Cognitive Modeling and Computational Linguistics


The semantic augmentation of a psycholinguistically-motivated syntactic formalism
Asad Sayeed | Vera Demberg
Proceedings of the Fourth Annual Workshop on Cognitive Modeling and Computational Linguistics (CMCL)

An opinion about opinions about opinions: subjectivity and the aggregate reader
Asad Sayeed
Proceedings of the 2013 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies


Incremental Neo-Davidsonian semantic construction for TAG
Asad Sayeed | Vera Demberg
Proceedings of the 11th International Workshop on Tree Adjoining Grammars and Related Formalisms (TAG+11)

Syntactic Surprisal Affects Spoken Word Duration in Conversational Contexts
Vera Demberg | Asad Sayeed | Philip Gorinski | Nikolaos Engonopoulos
Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning

Grammatical structures for word-level sentiment detection
Asad Sayeed | Jordan Boyd-Graber | Bryan Rusk | Amy Weinberg
Proceedings of the 2012 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies


Crowdsourcing syntactic relatedness judgements for opinion mining in the study of information technology adoption
Asad B. Sayeed | Bryan Rusk | Martin Petrov | Hieu C. Nguyen | Timothy J. Meyer | Amy Weinberg
Proceedings of the 5th ACL-HLT Workshop on Language Technology for Cultural Heritage, Social Sciences, and Humanities


“Expresses-an-opinion-about”: using corpus statistics in an information extraction approach to opinion mining
Asad B. Sayeed | Hieu C. Nguyen | Timothy J. Meyer | Amy Weinberg
Coling 2010: Posters

Crowdsourcing the evaluation of a domain-adapted named entity recognition system
Asad B. Sayeed | Timothy J. Meyer | Hieu C. Nguyen | Olivia Buzek | Amy Weinberg
Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics


Arabic Cross-Document Coreference Resolution
Asad Sayeed | Tamer Elsayed | Nikesh Garera | David Alexander | Tan Xu | Doug Oard | David Yarowsky | Christine Piatko
Proceedings of the ACL-IJCNLP 2009 Conference Short Papers


Minimalist Parsing of Subjects Displaced from Embedded Clauses in Free Word Order Languages
Asad B. Sayeed
Proceedings of the ACL Student Research Workshop