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Found 56 papers, 24 papers with code
How far can we get with one GPU in 100 hours? CoAStaL at MultiIndicMT Shared Task
This work shows that competitive translation results can be obtained in a constrained setting by incorporating the latest advances in memory and compute optimization.
A Multilingual Benchmark for Probing Negation-Awareness with Minimal Pairs
Negation is one of the most fundamental concepts in human cognition and language, and several natural language inference (NLI) probes have been designed to investigate pretrained language models’ ability to detect and reason with negation.
Moses and the Character-Based Random Babbling Baseline: CoAStaL at AmericasNLP 2021 Shared Task
We evaluated a range of neural machine translation techniques developed specifically for low-resource scenarios.
Does Mapo Tofu Contain Coffee? Probing LLMs for Food-related Cultural Knowledge
Recent studies have highlighted the presence of cultural biases in Large Language Models (LLMs), yet often lack a robust methodology to dissect these phenomena comprehensively.
UniMEEC: Towards Unified Multimodal Emotion Recognition and Emotion Cause
In this paper, we propose a Unified Multimodal Emotion recognition and Emotion-Cause analysis framework (UniMEEC) to explore the causality and complementarity between emotion and emotion cause.
Emotion-Cause Pair Extraction
Emotion Recognition in Conversation
+1
Exploring Visual Culture Awareness in GPT-4V: A Comprehensive Probing
Pretrained large Vision-Language models have drawn considerable interest in recent years due to their remarkable performance.
Bridging Cultural Nuances in Dialogue Agents through Cultural Value Surveys
The cultural landscape of interactions with dialogue agents is a compelling yet relatively unexplored territory.
CreoleVal: Multilingual Multitask Benchmarks for Creoles
Creoles represent an under-explored and marginalized group of languages, with few available resources for NLP research.
Cultural Adaptation of Recipes
We introduce a new task involving the translation and cultural adaptation of recipes between Chinese and English-speaking cuisines.
Rethinking Relation Classification with Graph Meaning Representations
In the field of natural language understanding, the intersection of neural models and graph meaning representations (GMRs) remains a compelling area of research.
Cultural Compass: Predicting Transfer Learning Success in Offensive Language Detection with Cultural Features
The increasing ubiquity of language technology necessitates a shift towards considering cultural diversity in the machine learning realm, particularly for subjective tasks that rely heavily on cultural nuances, such as Offensive Language Detection (OLD).
Geo-Encoder: A Chunk-Argument Bi-Encoder Framework for Chinese Geographic Re-Ranking
Chinese geographic re-ranking task aims to find the most relevant addresses among retrieved candidates, which is crucial for location-related services such as navigation maps.
On Evaluating Multilingual Compositional Generalization with Translated Datasets
To address this limitation, we craft a faithful rule-based translation of the MCWQ dataset from English to Chinese and Japanese.
What does the Failure to Reason with "Respectively" in Zero/Few-Shot Settings Tell Us about Language Models?
Humans can effortlessly understand the coordinate structure of sentences such as "Niels Bohr and Kurt Cobain were born in Copenhagen and Seattle, respectively".
What's the Meaning of Superhuman Performance in Today's NLU?
In the last five years, there has been a significant focus in Natural Language Processing (NLP) on developing larger Pretrained Language Models (PLMs) and introducing benchmarks such as SuperGLUE and SQuAD to measure their abilities in language understanding, reasoning, and reading comprehension.
Pay More Attention to Relation Exploration for Knowledge Base Question Answering
In this study, we propose a novel framework, RE-KBQA, that utilizes relations in the knowledge base to enhance entity representation and introduce additional supervision.
Cross-Cultural Transfer Learning for Chinese Offensive Language Detection
Detecting offensive language is a challenging task.
Cultural Vocal Bursts Intensity Prediction
Few-Shot Learning
+1
Assessing Cross-Cultural Alignment between ChatGPT and Human Societies: An Empirical Study
The recent release of ChatGPT has garnered widespread recognition for its exceptional ability to generate human-like responses in dialogue.
A Two-Sided Discussion of Preregistration of NLP Research
Van Miltenburg et al. (2021) suggest NLP research should adopt preregistration to prevent fishing expeditions and to promote publication of negative results.
Towards Climate Awareness in NLP Research
We argue that this deficiency is one of the reasons why very few publications in NLP report key figures that would allow a more thorough examination of environmental impact.
Evaluating Deep Taylor Decomposition for Reliability Assessment in the Wild
We argue that we need to evaluate model interpretability methods 'in the wild', i. e., in situations where professionals make critical decisions, and models can potentially assist them.
Generalized Quantifiers as a Source of Error in Multilingual NLU Benchmarks
Logical approaches to representing language have developed and evaluated computational models of quantifier words since the 19th century, but today's NLU models still struggle to capture their semantics.
Challenges and Strategies in Cross-Cultural NLP
Various efforts in the Natural Language Processing (NLP) community have been made to accommodate linguistic diversity and serve speakers of many different languages.
Can Language Models Encode Perceptual Structure Without Grounding? A Case Study in Color
Pretrained language models have been shown to encode relational information, such as the relations between entities or concepts in knowledge-bases -- (Paris, Capital, France).
Compositional Generalization in Multilingual Semantic Parsing over Wikidata
We introduce such a dataset, which we call Multilingual Compositional Wikidata Questions (MCWQ), and use it to analyze the compositional generalization of semantic parsers in Hebrew, Kannada, Chinese and English.
Meaning Representation of Numeric Fused-Heads in UCCA
We exhibit that the implicit UCCA parser does not address numeric fused-heads (NFHs) consistently, which could result either from inconsistent annotation, insufficient training data or a modelling limitation.
Great Service! Fine-grained Parsing of Implicit Arguments
Broad-coverage meaning representations in NLP mostly focus on explicitly expressed content.
Scaling Creative Inspiration with Fine-Grained Functional Aspects of Ideas
Large repositories of products, patents and scientific papers offer an opportunity for building systems that scour millions of ideas and help users discover inspirations.
Does injecting linguistic structure into language models lead to better alignment with brain recordings?
We evaluate across two fMRI datasets whether language models align better with brain recordings, if their attention is biased by annotations from syntactic or semantic formalisms.
Cross-lingual Semantic Representation for NLP with UCCA
This is an introductory tutorial to UCCA (Universal Conceptual Cognitive Annotation), a cross-linguistically applicable framework for semantic representation, with corpora annotated in English, German and French, and ongoing annotation in Russian and Hebrew.
Comparison by Conversion: Reverse-Engineering UCCA from Syntax and Lexical Semantics
Building robust natural language understanding systems will require a clear characterization of whether and how various linguistic meaning representations complement each other.
HUJI-KU at MRP 2020: Two Transition-based Neural Parsers
This paper describes the HUJI-KU system submission to the shared task on CrossFramework Meaning Representation Parsing (MRP) at the 2020 Conference for Computational Language Learning (CoNLL), employing TUPA and the HIT-SCIR parser, which were, respectively, the baseline system and winning system in the 2019 MRP shared task.
MRP 2020: The Second Shared Task on Cross-Framework and Cross-Lingual Meaning Representation Parsing
Extending a similar setup from the previous year, five distinct approaches to the representation of sentence meaning in the form of directed graphs were represented in the English training and evaluation data for the task, packaged in a uniform graph abstraction and serialization; for four of these representation frameworks, additional training and evaluation data was provided for one additional language per framework.
HUJI-KU at MRP~2020: Two Transition-based Neural Parsers
This paper describes the HUJI-KU system submission to the shared task on Cross-Framework Meaning Representation Parsing (MRP) at the 2020 Conference for Computational Language Learning (CoNLL), employing TUPA and the HIT-SCIR parser, which were, respectively, the baseline system and winning system in the 2019 MRP shared task.
Ranked #2 on
Semantic Parsing
on DRG (english, MRP 2020)
Joint Semantic Analysis with Document-Level Cross-Task Coherence Rewards
Coreference resolution and semantic role labeling are NLP tasks that capture different aspects of semantics, indicating respectively, which expressions refer to the same entity, and what semantic roles expressions serve in the sentence.
K\opsala: Transition-Based Graph Parsing via Efficient Training and Effective Encoding
We present K{\o}psala, the Copenhagen-Uppsala system for the Enhanced Universal Dependencies Shared Task at IWPT 2020.
Refining Implicit Argument Annotation for UCCA
Predicate-argument structure analysis is a central component in meaning representations of text.
Køpsala: Transition-Based Graph Parsing via Efficient Training and Effective Encoding
We present K{\o}psala, the Copenhagen-Uppsala system for the Enhanced Universal Dependencies Shared Task at IWPT 2020.
Lexical Semantic Recognition
In lexical semantics, full-sentence segmentation and segment labeling of various phenomena are generally treated separately, despite their interdependence.
Ranked #1 on
Natural Language Understanding
on STREUSLE
MRP 2019: Cross-Framework Meaning Representation Parsing
The 2019 Shared Task at the Conference for Computational Language Learning (CoNLL) was devoted to Meaning Representation Parsing (MRP) across frameworks.
TUPA at MRP 2019: A Multi-Task Baseline System
This paper describes the TUPA system submission to the shared task on Cross-Framework Meaning Representation Parsing (MRP) at the 2019 Conference for Computational Language Learning (CoNLL).
Ranked #2 on
UCCA Parsing
on CoNLL 2019
Rewarding Coreference Resolvers for Being Consistent with World Knowledge
Unresolved coreference is a bottleneck for relation extraction, and high-quality coreference resolvers may produce an output that makes it a lot easier to extract knowledge triples.
Argument Invention from First Principles
In this work we aim to explicitly define a taxonomy of such principled recurring arguments, and, given a controversial topic, to automatically identify which of these arguments are relevant to the topic.
Content Differences in Syntactic and Semantic Representation
Syntactic analysis plays an important role in semantic parsing, but the nature of this role remains a topic of ongoing debate.
The Language of Legal and Illegal Activity on the Darknet
The non-indexed parts of the Internet (the Darknet) have become a haven for both legal and illegal anonymous activity.
Content Differences in Syntactic and Semantic Representations
Syntactic analysis plays an important role in semantic parsing, but the nature of this role remains a topic of ongoing debate.
SemEval-2019 Task 1: Cross-lingual Semantic Parsing with UCCA
We present the SemEval 2019 shared task on UCCA parsing in English, German and French, and discuss the participating systems and results.
Universal Dependency Parsing with a General Transition-Based DAG Parser
This paper presents our experiments with applying TUPA to the CoNLL 2018 UD shared task.
SemEval 2019 Shared Task: Cross-lingual Semantic Parsing with UCCA - Call for Participation
Given the success of recent semantic parsing shared tasks (on SDP and AMR), we expect the task to have a significant contribution to the advancement of UCCA parsing in particular, and semantic parsing in general.
Multitask Parsing Across Semantic Representations
The ability to consolidate information of different types is at the core of intelligence, and has tremendous practical value in allowing learning for one task to benefit from generalizations learned for others.
Ranked #3 on
UCCA Parsing
on SemEval 2019 Task 1
A Transition-Based Directed Acyclic Graph Parser for UCCA
We present the first parser for UCCA, a cross-linguistically applicable framework for semantic representation, which builds on extensive typological work and supports rapid annotation.
Ranked #4 on
UCCA Parsing
on SemEval 2019 Task 1
