Timothée Bernard and Grégoire Winterstein. Bridges and gaps between formal and computational linguistics 

(Workshop website:

While computational linguistics is historically rooted in formal linguistics, it might seem that the distance between these two fields has only grown larger and larger.

The goal of this workshop is to consider whether this impression is correct in the light of both recent developments and long-standing approaches.

Indeed, while we are currently witnessing a growing interest within formal linguistics in both explaining the remarkable successes of neural-based language models and uncovering their limitations, one should not forget the contribution to theoretical linguistics provided, for example, by the computational implementation of grammatical formalisms.

And while neural-based methods have recently received the lion's share of the public attention, interpretable models based on symbolic methods are still relevant and widely used in the natural language processing industry.

This workshop is intended to make members of the aforementioned scientific communities meet and share their perspectives on these topics and related areas.


Stergios Chatzikyriakidis and Aikaterini-Lida Kalouli. Natural Logic Meets Machine Learning III (NALOMA’22)

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Recently, there has been a surge of interest in tasks targeting Natural Language Understanding and Reasoning. Although deep learning models have achieved human-like performance in many such tasks, it has also been repeatedly shown that they lack the precision, generalization power and reasoning capabilities found in more traditional, symbolic approaches. Thus, current research has started employing hybrid methods, combining the strengths of each tradition and mitigating its weaknesses. This workshop would like to promote this research direction and foster fruitful dialog between the two disciplines by bringing together researchers working on hybrid methods in any subfield of NLU.


Kristina Liefke and Justin D'Ambrosio. The Semantics of Imagination

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Imagination has recently entered center stage in semantics and the philosophy of language. While relevant work has focused on diverse aspects of imagination, it has left the particular semantic properties of the verb 'imagine' underexplored. These include the wide selectional flexibility and interesting entailment pattern of 'imagine', the referential dependence of 'imagine'-complements on experience, and the ability of 'imagine'-reports to encode perspective. Since these properties differ from those of standard responsive predicates, parasitic attitudes, and perspectival expressions, they resist modelling through the familiar semantic tools.

Our workshop aims to bring together work on the semantics of imagination that fills this modelling gap. Its master objective is to identify the particular contribution that 'imagine' and (the different constituents in) its complements make to the complex truth-conditions of imagination reports. To achieve this, we will also solicit papers that contrast the semantics of 'imagine' with that of related verbs, esp. 'remember', 'dream', and 'see'.


Michaël Moortgat and Gijs Wijnholds. End-to-End Compositional Models of Vector-Based Semantics

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Compositionality models the syntax-semantics interface as a structure-preserving map relating syntactic categories (types) and derivations to their counterparts in a corresponding meaning algebra. In a distributional setting, the basic building blocks are vector-based representations of word meanings (embeddings) obtained from data. These word meanings then have to be combined into meanings for larger expressions in a way that reflects the structure of their syntactic composition.

The workshop focuses on *end-to-end* implementations of such vector-based compositional architectures. This means not only the elementary word embeddings are obtained from data, but also the categories/types and their internal composition so that neural methods can then be applied to learn how the structure of syntactic derivations can be systematically mapped to operations on the data-driven word representations. For this last step, the workshop invites approaches that do not require the semantic operations to be linear maps since restricting the meaning algebra to finite dimensional vector spaces and linear maps means that vital information encoded in syntactic derivations may be lost in translation.

On the evaluation side, we welcome work on modern NLP tasks for evaluating sentence embeddings such as Natural Language Inference, sentence-level classification, and sentence disambiguation tasks. Special interest goes out to work that uses compositionality to investigate the syntactic sensitivity of large-scale language models.

Workshop contributions and invited talks will address the above challenges both from a theoretical and from a practical point of view.


Ielka van der Sluis and James Pustejovsky. Annotation, Recognition and Evaluation of Actions II (AREA-II)

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AREA II is the follow up on the first AREA meeting at LREC 2018 .

There has recently been increased interest in modeling actions, as described by natural language expressions and gestures, and as depicted by images and videos. Additionally, action modeling has emerged as an important topic in robotics and HCI. The goal of the AREA II workshop is to gather and discuss advances in research areas where actions are paramount e.g., virtual embodied agents, robotics, HRI, human-computer communication, as well as modeling multimodal human-human interactions involving actions. Action modeling is an inherently multi-disciplinary area, involving contributions from computational linguistics, AI, semantics, robotics, psychology, and formal logic.