Multilingual and Code-Switched Sentence Ordering (2024.starsem-1)

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Challenge: Prior research has focused on English language structures and multilingual contexts . however, there are several shortcomings with specialized sentence ordering models and advanced Large Language Models like GPT-4.
Approach: They propose a multilingual sentence order task that extends SO to diverse narratives across 12 languages and code-switched texts.
Outcome: The proposed task extends SO to diverse narratives across 12 languages, including challenging code-switched texts.

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Inducing Language-Agnostic Multilingual Representations (2021.starsem-1)

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Challenge: Cross-lingual representations have the potential to make NLP techniques available to the vast majority of languages in the world, but they currently require large pretraining corpora or access to typologically similar languages.
Approach: They propose to remove language identity signals from multilingual embeddings by re-aligning vector spaces of target languages to a pivot source language and removing language-specific means and variances.
Outcome: The proposed approaches reduce cross-lingual transfer gap by 8.9 points (m-BERT) and 18.2 points (XLM-R) on average across all tasks and languages.
Script Parsing with Hierarchical Sequence Modelling (2021.starsem-1)

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Challenge: Script knowledge is a category of commonsense knowledge that describes how people conduct everyday activities sequentially.
Approach: They propose a hierarchical sequence model and transfer learning to do script parsing with a sequence model that accurately tags script participants.
Outcome: The proposed model improves state of the art of event parsing by over 16 points F-score and, for the first time, accurately tags script participants.
Does Character-level Information Always Improve DRS-based Semantic Parsing? (2023.starsem-1)

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Challenge: incorporating character-level information does not improve the performance in English and German, and is not sensitive to correct character order in Dutch.
Approach: They propose to incorporate character-level representations into a neural semantic parser for Discourse Representation Structures and to test their performance using order of character sequences.
Outcome: The proposed parser improves in English, German, Dutch, and Italian in four languages.
Dyna-bAbI: unlocking bAbI’s potential with dynamic synthetic benchmarking (2022.starsem-1)

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Challenge: Controlled synthetic tasks are an important resource for diagnosing model behavior.
Approach: They propose a framework that provides fine-grained control over task generation in bAbI.
Outcome: The proposed framework provides fine-grained control over task generation in the bAbI benchmark.
What do Large Language Models Learn about Scripts? (2022.starsem-1)

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Challenge: Script Knowledge is important for language understanding but expensive to produce manually and difficult to induce from text due to reporting bias.
Approach: They propose a pipeline-based script induction framework which can generate good quality ESDs for unseen scenarios.
Outcome: The proposed framework produces good quality ESDs for unseen scenarios, but manual evaluation shows there is room for improvement.
Exploring Factual Entailment with NLI: A News Media Study (2024.starsem-1)

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Challenge: Recent studies have focused on the relationship between factuality and Natural Language Inference (NLI).
Approach: They propose a novel annotation scheme that models factual rather than textual entailment and use it to annotate a dataset of naturally occurring sentences from news articles.
Outcome: The proposed annotation scheme can be used to model factual relationships on a dataset of naturally occurring sentences from news articles.
Did the Cat Drink the Coffee? Challenging Transformers with Generalized Event Knowledge (2021.starsem-1)

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Challenge: Prior work has explored the ability of computational models to predict word semantic fit with a given predicate.
Approach: They compare Transformers Language Models to SDM to assess their performance . they found that TLMs do not capture important aspects of event knowledge . people can discriminate between typical and atypical events, they say .
Outcome: The proposed models can achieve comparable performance to SDM, but they lack important aspects of event knowledge.
Word-Label Alignment for Event Detection: A New Perspective via Optimal Transport (2022.starsem-1)

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Challenge: Event Detection (ED) is a critical task in Information Extraction.
Approach: They propose a word-label alignment task for event detecting . they propose to incorporate word-labeled alignment biases into the equation .
Outcome: The proposed model facilitates incorporation of word-label alignment biases on a benchmark dataset to demonstrate its effectiveness.
Guiding Zero-Shot Paraphrase Generation with Fine-Grained Control Tokens (2023.starsem-1)

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Challenge: Sequence-to-sequence paraphrase generation models struggle with the generation of diverse paraphrases.
Approach: They propose a translation-based guided paraphrase generation model that learns useful features for promoting surface form variation in generated paraphrases from cross-lingual parallel data.
Outcome: The proposed model learns useful features for promoting surface form variation in generated paraphrases from cross-lingual parallel data.
VOLIMET: A Parallel Corpus of Literal and Metaphorical Verb-Object Pairs for English–German and English–French (2024.starsem-1)

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Challenge: Metaphorical language is a complex interplay of cultural and linguistic elements that characterizes metaphorical language . a corpus of parallel sentences containing gold standard alignments of metaphorical verb-object pairs and literal paraphrases is presented .
Approach: They propose to analyze metaphorical verb-object pairs and literal paraphrases in parallel sentences from English to German and French.
Outcome: The proposed corpus of 2,916 parallel sentences reveals monolingual patterns for metaphorical vs. literal uses in English . cross-lingually, the results show a rich variability in translations as well as different behaviors for the two target languages .

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