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We present a novel dataset for physical and abstract plausibility of events in English. Based on naturally occurring sentences extracted from Wikipedia, we infiltrate degrees of abstractness, and automatically generate perturbed pseudo-implausible events. We annotate a filtered and balanced subset for plausibility using crowd-sourcing, and perform extensive cleansing to ensure annotation quality. In-depth quantitative analyses indicate that annotators favor plausibility over implausibility and disagree more on implausible events. Furthermore, our plausibility dataset is the first to capture abstractness in events to the same extent as concreteness, and
we find that event abstractness has an impact on plausibility ratings: 
more concrete event participants trigger a perception of implausibility.

ACL 2023

A Dataset for Physical and Abstract Plausibility and Sources of Human Disagreement

abstractness

human annotation

plausibility

disagreement

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workshop paper

### Welcome to ACL 2023, the 61st Annual Meeting of the Association for Computational Linguistics! 
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Multimodal machine translation (MMT) aims to improve translation quality by incorporating information from other modalities, such as vision. Previous MMT systems focus on better access and use of visual information and tend to validate their methods on image-related datasets. However, these studies face two challenges. First, they can only utilize a limited amount of data that is composed of bilingual texts and images (referred to as "triple data"), which is scarce. Second, current benchmarks for MMT are restricted and do not correspond to realistic scenarios. Therefore, this paper correspondingly establishes new methods and a new dataset for MMT. We propose a novel framework for MMT that addresses these challenges by utilizing large-scale non-triple data, such as monolingual image-text and parallel text-only data. Additionally, we construct a new e-commercial multimodal translation dataset, named EMMT, of which the test set is specifically designed to include ambiguous words that require visual context for accurate translation. Experiments show that our method is well-suited for real-world scenarios and can significantly improve translation performance with more non-triple data. In addition, our model also rivals or surpasses various SOTA models in conventional multimodal translation benchmarks.

<iframe src="https://app.sli.do/event/eX4PwSmcCc1EvCqjxcYdKB/embed/polls/5b3d6230-8f15-4c61-814d-15b4162a73e3" width="300" height="400"></iframe>

Beyond Triplet: Leveraging the Most Data for Multimodal Machine Translation

The Turkish particle dA is a focus-associated enclitic, and it can act as a discourse connective conveying multiple senses, like additive, contrastive, causal etc. Like many other linguistic expressions, it is subject to usage ambiguity and creates a challenge in natural language automatization tasks. For the first time, we annotate the discourse and non-discourse connnective occurrences of dA in Turkish with the PDTB principles. Using a minimal set of linguistic features, we develop binary classifiers to distinguish its discourse connective usage from its other usages. We show that despite its ability to cliticize to any syntactic type, variable position in the sentence and having a wide argument span, its discourse/non-discourse connective usage can be annotated reliably and its discourse usage can be disambiguated by exploiting local cues.

Annotating and Disambiguating the Discourse Usage of the Enclitic dA in Turkish

This work presents two corpora based on excerpts from two German novels with an infor- mal narration style. We performed fine-grained multi-layer annotations of animate referents, assigning local and global prominence-lending features to the annotated referring expressions. In addition, our corpora include annotations of intra-sentential segments, which can serve as a more reliable unit of length measurement. Furthermore, we present two exemplary studies demonstrating how to use these corpora.

Multi-layered Annotation of Conversation-like Narratives in German

Most tasks in NLP require labeled data. Data labeling is often done on crowdsourcing platforms due to scalability reasons. However, publishing data on public platforms can only be done if no privacy-relevant information is included.
Textual data often contains sensitive information like person names or locations. In this work, we investigate how removing personally identifiable information (PII) as well as applying differential privacy (DP) rewriting can enable text with privacy-relevant information to be used for crowdsourcing. We find that DP-rewriting before crowdsourcing can preserve privacy while still leading to good label quality
for certain tasks and data. PII-removal led to good label quality in all examined tasks, however, there are no privacy guarantees given.

Crowdsourcing on Sensitive Data with Privacy-Preserving Text Rewriting

The availability of annotated legal corpora is crucial for a number of tasks. Annotation is mostly assumed to be a straightforward task: as long as the annotation scheme is well defined and the guidelines are clear, annotators are expected to agree on the labels. This is not always the case, especially in legal annotation, which can be extremely difficult even for expert annotators. We propose a legal annotation procedure that takes into account annotator certainty and improves it through negotiation. We also collect annotator feedback and show that our approach contributes to a positive annotation environment. Our work invites reflection on often neglected ethical concerns regarding legal annotation.

Annotators-in-the-loop: Testing a Novel Annotation Procedure on Italian Case Law

We reports on a three-part series of original methods geared towards producing semantic annotations for the decompositional marker "again". The three methods are (i) exhaustive expert annotation based on a comprehensive set of guidelines, (ii) extension of expert annotation by predicting presuppositions with a Multinomial Naïve Bayes classifier in the context of a meta-analysis to optimize feature selection and (iii) quality-controlled crowdsourcing with ensuing evaluation and KMeans clustering of annotation vectors.

Annotating Decomposition in Time: Three Approaches for Again

The annotation task we elaborated aims at describing the contextual factors that influence the appearance and interpretation of moral predicates, in newspaper articles on police brutality, in French and in English. The paper provides a brief review of the literature on moral predicates and their relation with context. The paper also describes the elaboration of the corpus and the ontology. Our hypothesis is that the use of moral adjectives and their appearance in context could change depending on the political orientation of the journal. We elaborated an annotation task to investigate the precise contexts discussed in articles on police brutality. The paper concludes by describing the study and the annotation task in details.

Pragmatic Annotation of Articles Related to Police Brutality

We present the RST Continuity Corpus (RST-CC), a corpus of discourse relations annotated for continuity dimensions. Continuity or discontinuity (maintaining or shifting deictic centres across discourse segments) is an important property of discourse relations, but the two are correlated in greatly varying ways. To analyse this correlation, the relations in the RST-CC are annotated using operationalised versions of Givón’s (1993) continuity dimensions. We also report on the inter-annotator agreement, and discuss recurrent annotation issues. First
results show substantial variation of continuity dimensions within and across relation types.

The RST Continuity Corpus

In this paper we address the scarcity of annotated data for NArabizi, a Romanized form of North African Arabic used mostly on social media, which poses challenges for Natural Language Processing (NLP). We introduce an enriched version of NArabizi Treebank (Seddah et al., 2020) with three main contributions: the addition of two novel annotation layers (named entity recognition and offensive language detection) and a re-annotation of the tokenization, morpho-syntactic and syntactic layers that ensure annotation consistency. Our experimental results, using different tokenization schemes, showcase the value of our contributions and highlight the impact of working with non-gold tokenization for NER and dependency parsing. To facilitate future research, we make these annotations publicly available. Our enhanced NArabizi Treebank paves the way for creating sophisticated language models and NLP tools for this under-represented language.

Enriching the NArabizi Treebank: A Multifaceted Approach to Supporting an Under-Resourced Language

Coreference Resolution is a well studied problem in NLP. While widely studied for English and other resource-rich languages, research on coreference resolution in Bengali largely remains unexplored due to the absence of relevant datasets. Bengali, being a low-resource language, exhibits greater morphological richness compared to English. In this article, we introduce a new dataset, BenCoref, comprising coreference annotations for Bengali texts gathered from four distinct domains. This relatively small dataset contains 5200 mention annotations forming 502 mention clusters within 48,569 tokens. We describe the process of creating this dataset and report performance of multiple models trained using BenCoref. We
expect that our work provides some valuable insights on the variations in coreference phenomena across several domains in Bengali and encourages the development of additional resources for Bengali. Furthermore, we found
poor crosslingual performance at zero-shot setting from English, highlighting the need for more language-specific resources for this task.

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Beyond Triplet: Leveraging the Most Data for Multimodal Machine Translation

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