Canada

Automatic image comprehension is an important yet challenging task that includes identifying actions in an image and corresponding action participants. Most current approaches to this task, now termed \textbf{G}rounded \textbf{S}ituation \textbf{R}ecognition (GSR), start by predicting a verb that describes the action and then predict the nouns that can participate in the action as arguments to the verb. This problem formulation limits each image to a single action even though several actions could be depicted. In contrast, text-based \textbf{S}emantic \textbf{R}ole \textbf{L}abeling (SRL) aims to label all actions in a sentence, typically resulting in at least two or three predicate argument structures per sentence. We hypothesize that expanding GSR to follow the more liberal SRL text-based approach to action and participant identification could improve image comprehension results. To test this hypothesis and to preserve generalization capabilities, we use general-purpose vision and language components as a front-end. This paper presents our results, a substantial 28.6 point jump in performance on the SWiG dataset, which confirm our hypothesis. We also discuss the benefits of loosely coupled broad-coverage off-the-shelf components which generalized well to out of domain images, and can decrease the need for manual image semantic role annotation.

ACL 2023

CRAPES:Cross-modal Annotation Projection for Visual Semantic Role Labeling

grounded situation recognition

visual semantic role labeling

We are pleased to announce the Fortieth AAAI Conference on Artificial Intelligence (AAAI-26), which will be held in Singapore EXPO from January 20 to January 27, 2026.

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AAAI 2026

We are pleased to announce the Fortieth AAAI Conference on Artificial Intelligence (AAAI-26), which will be held in Singapore EXPO from January 20 to January 27, 2026.

workshop paper

### Welcome to ACL 2023, the 61st Annual Meeting of the Association for Computational Linguistics! 
 The conference will be held in Toronto, Canada, July 9-14, 2023. 
Following the succession of the recent conferences in our field, ACL 2023 will adopt a hybrid format.
While the impact of Covid has considerably diminished in terms of traveling, obtaining visas to Canada
entails a very long process. Moreover, the global economic conditions pose challenges for many individuals to travel to conferences. Recognizing these circumstances, we know many participants may not be
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current registered participants, approxiately 30% have chosen to attend the conference virtually. Whether
you join us in person or virtually, we sincerely hope everyone has a remarkable conference experience. 
This General Chair’s message is where I express my gratitude to the many individuals who have made
enormous contributions to the conference over the past year.

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The Association for Computational Linguistics (ACL) is the premier international scientific and professional society for people working on computational problems involving human language, a field often referred to as either computational linguistics or natural language processing.

Aspect sentiment quad prediction (ASQP) analyzes the aspect terms, opinion terms, sentiment polarity, and aspect categories in a text. One challenge in this task is the scarcity of data owing to the high annotation cost. Data augmentation techniques are commonly used to address this issue. However, existing approaches simply rewrite texts in the training data, restricting the semantic diversity of the generated data and impairing the quality due to the inconsistency between text and quads. To address these limitations, we augment quads and train a quads-to-text model to generate corresponding texts. Furthermore, we designed novel strategies to filter out low-quality data and balance the sample difficulty distribution of the augmented dataset. Empirical studies on two ASQP datasets demonstrate that our method outperforms other data augmentation methods and achieves state-of-the-art performance on the benchmarks.

Generative Data Augmentation for Aspect Sentiment Quad Prediction

The task of entity state tracking aims to automatically analyze procedural texts – texts that describe a step-by-step process (e.g. a baking recipe). Specifically, the goal is to track various states of the entities participating in a given process. Some of the challenges for this NLP task include annotated data scarcity and annotators’ reliance on commonsense knowledge to annotate implicit state information. Zhang et al. (2021) successfully incorporated commonsense entity-centric knowledge from ConceptNet into their BERT-based neurosymbolic architecture. Since English mostly encodes state change information in verbs, we attempted to test whether injecting semantic knowledge of events (retrieved from the state-of-the-art Verb-
Net parser) into a neural model can also improve the performance on this task. To achieve this, we adapt the methodology introduced by Zhang et al. (2021) for incorporating symbolic entity information from ConceptNet to the incorporation
of VerbNet event semantics. We evaluate the performance of our model on the
ProPara dataset (Mishra et al., 2018). In addition, we introduce LEXIS, our purely symbolic model for entity state tracking that uses a simple set of case statements, and is informed mostly by linguistic knowledge retrieved from various computational lexical resources. Our approach is inherently domain-agnostic, and our model is explainable and achieves state-of-the-art results on the Recipes dataset (Bosselut
et al., 2017).

Event Semantic Knowledge in Procedural Text Understanding

Multimodal embeddings aim to enrich the semantic information in neural representations of language compared to text-only models. While different embeddings exhibit different applicability and performance on downstream tasks, little is known about the systematic representation differences attributed to the visual modality. Our paper compares word embeddings from three vision-and-language models (CLIP, OpenCLIP and Multilingual CLIP) and three text-only models, with static (FastText) as well as contextual representations (multilingual BERT; XLM-RoBERTa). This is the first large-scale study of the effect of visual grounding on language representations, including 46 semantic parameters. We identify meaning properties and relations that characterize words whose embeddings are most affected by the inclusion of visual modality in the training data; that is, points where visual grounding turns out most important. We find that the effect of visual modality correlates most with denotational semantic properties related to concreteness, but is also detected for several specific semantic classes, as well as for valence, a sentiment-related connotational property of linguistic expressions.

Leverage Points in Modality Shifts: Comparing Language-only and Multimodal Word Representations

Idioms such as ''call it a day'' and ''piece of cake''', are ubiquitous in natural language. How do Transformer language models process idioms? This study examines this question by analysing three models - BERT, Multilingual BERT, and DistilBERT. We compare the embeddings of idiomatic and literal expressions across all layers of the networks at both the sentence and word levels. Additionally, we investigate the attention directed from other sentence tokens towards a word within an idiom as opposed to in a literal context. Results indicate that while the three models exhibit slightly different internal mechanisms, they all represent idioms distinctively compared to literal language, with attention playing a critical role. These findings suggest that idioms are semantically and syntactically idiosyncratic, not only for humans but also for language models.

How Are Idioms Processed Inside Transformer Language Models?


Words of Estimative Probability (WEP) are phrases used to express the plausibility of a statement. Examples include terms like probably, maybe, likely, doubt, unlikely, and impossible. Surveys have shown that human evaluators tend to agree when assigning numerical probability levels to these WEPs. For instance, the term highly likely equates to a median probability of 0.90±0.08 according to a survey by Fagen-Ulmschneider (2015). In this study, our focus is to gauge the competency of neural language processing models in accurately capturing the consensual probability level associated with each WEP. Our first approach is utilizing the UNLI dataset (Chen et al., 2020), which links premises and hypotheses with their perceived joint probability p. From this, we craft prompts in the form: "[PREMISE]. [WEP], [HYPOTHESIS]." This allows us to evaluate whether language models can predict if the consensual probability level of a WEP aligns closely with p. In our second approach, we develop a dataset based on WEP-focused probabilistic reasoning to assess if language models can logically process WEP compositions. For example, given the prompt "[EVENTA] is likely. [EVENTB] is impossible.", a well-functioning language model should not conclude that [EVENTA&B] is likely. Through our study, we observe that both tasks present challenges to out-of-the-box English language models. However, we also demonstrate that fine-tuning these models can lead to significant and transferable improvements.

Probing neural language models for understanding of words of estimative probability

Prior work has shown that coupling sequential latent variable models with semantic ontological knowledge can improve the representational capabilities of event modeling approaches. In this work, we present a novel, doubly hierarchical, semi-supervised event modeling framework that provides structural hierarchy while
also accounting for ontological hierarchy. Our approach consists of multiple layers of structured latent variables, where each successive layer compresses and abstracts the previous layers. We guide this compression through the injection of structured ontological knowledge that is defined at the type level of events: importantly, our model allows for partial injection of semantic knowledge and it does not depend on observing instances at any particular level of the semantic ontology. Across two
different datasets and four different evaluation metrics, we demonstrate that our approach is able to out-perform the previous state-of-the-art approaches by up to 8.5%, demonstrating the benefits of structured and semantic hierarchical knowledge for event modeling.

Semantically-informed Hierarchical Event Modeling

Even in the era of massive language models, it has been suggested that character-level representations improve the performance of neural models. The state-of-the-art neural semantic parser for Discourse Representation Structures uses character-level representations, improving performance in the four languages (i.e., English, German, Dutch, and Italian) in the Parallel Meaning Bank dataset. However, how and why character-level information improves the parser’s performance remains unclear. This study provides an in-depth analysis of performance changes by order of character sequences. In the experiments, we compare F1-scores by shuffling the order and randomizing character sequences after testing the performance of character-level information. Our results indicate that incorporating character-level information does not improve the performance in English and German. In addition, we find that the parser is not sensitive to correct character order in Dutch. Nevertheless, performance improvements are observed when using character-level information.

Does Character-level Information Always Improve DRS-based Semantic Parsing?

We tackle the problem of monolingual phrase alignment conforming to syntactic structures. The existing method formalises the problem as unordered tree mapping; hence, the alignment quality is easily affected by syntactic ambiguities. We address this problem by expanding the method to align parse forests rather than 1-best trees, where syntactic structures and phrase alignment are simultaneously identified. The proposed method achieves efficient alignment by mapping forests on a packed structure. The experimental results indicated that our method improves the phrase alignment quality of the state-of-the-art method by aligning forests rather than 1-best trees.

Monolingual Phrase Alignment as Parse Forest Mapping

Stance detection deals with identifying an author's stance towards a target.
Most existing stance detection models are limited because they do not consider relevant contextual information which allows for inferring the stance correctly.
Complementary context can be found in knowledge bases but integrating the context into pretrained language models is non-trivial due to the graph structure of standard knowledge bases.
To overcome this, we explore an approach to integrate contextual information as text which allows for integrating contextual information from heterogeneous sources, such as structured knowledge sources and by prompting large language models.
Our approach can outperform competitive baselines on a large and diverse stance detection benchmark in a cross-target setup, i.e. for targets unseen during training.
We demonstrate that it is more robust to noisy context and can regularize for unwanted correlations between labels and target-specific vocabulary.
Finally, it is independent of the pretrained language model in use.

Robust Integration of Contextual Information for Cross-Target Stance Detection

Prior work typically describes out-of-domain (OOD) or out-of-distribution (OODist) samples as those that originate from dataset(s) or source(s) different from the training set but for the same task. When compared to in-domain (ID) samples, the models have been known to usually perform poorer on OOD samples, although this observation is not consistent. Another thread of research has focused on OOD detection, albeit mostly using supervised approaches. In this work, we first consolidate and present a systematic analysis of multiple definitions of OOD and OODist as discussed in prior literature. Then, we analyze the performance of a model under ID and OOD/OODist settings in a principled way. Finally, we seek to identify an unsupervised method for reliably identifying OOD/OODist samples without using a trained model. The results of our extensive evaluation using 12 datasets from 4 different tasks suggest the promising potential of unsupervised metrics in this task.

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