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Found 42 papers, 5 papers with code
From Zero to Hero: On the Limitations of Zero-Shot Language Transfer with Multilingual Transformers
Massively multilingual transformers (MMTs) pretrained via language modeling (e. g., mBERT, XLM-R) have become a default paradigm for zero-shot language transfer in NLP, offering unmatched transfer performance.
LexFit: Lexical Fine-Tuning of Pretrained Language Models
Inspired by prior work on semantic specialization of static word embedding (WE) models, we show that it is possible to expose and enrich lexical knowledge from the LMs, that is, to specialize them to serve as effective and universal {``}decontextualized{''} word encoders even when fed input words {``}in isolation{''} (i. e., without any context).
Is Supervised Syntactic Parsing Beneficial for Language Understanding Tasks? An Empirical Investigation
Traditional NLP has long held (supervised) syntactic parsing necessary for successful higher-level semantic language understanding (LU).
SemEval-2020 Task 2: Predicting Multilingual and Cross-Lingual (Graded) Lexical Entailment
The shared task spans three dimensions: (1) monolingual vs. cross-lingual LE, (2) binary vs. graded LE, and (3) a set of 6 diverse languages (and 15 corresponding language pairs).
XHate-999: Analyzing and Detecting Abusive Language Across Domains and Languages
We present XHate-999, a multi-domain and multilingual evaluation data set for abusive language detection.
Non-Linear Instance-Based Cross-Lingual Mapping for Non-Isomorphic Embedding Spaces
We present InstaMap, an instance-based method for learning projection-based cross-lingual word embeddings.
Bilingual Lexicon Induction
Cross-Lingual Word Embeddings
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Classification-Based Self-Learning for Weakly Supervised Bilingual Lexicon Induction
Effective projection-based cross-lingual word embedding (CLWE) induction critically relies on the iterative self-learning procedure.
Improving Bilingual Lexicon Induction with Unsupervised Post-Processing of Monolingual Word Vector Spaces
Work on projection-based induction of cross-lingual word embedding spaces (CLWEs) predominantly focuses on the improvement of the projection (i. e., mapping) mechanisms.
Cross-lingual Semantic Specialization via Lexical Relation Induction
Semantic specialization integrates structured linguistic knowledge from external resources (such as lexical relations in WordNet) into pretrained distributional vectors in the form of constraints.
SEAGLE: A Platform for Comparative Evaluation of Semantic Encoders for Information Retrieval
We introduce Seagle, a platform for comparative evaluation of semantic text encoding models on information retrieval (IR) tasks.
SenZi: A Sentiment Analysis Lexicon for the Latinised Arabic (Arabizi)
As such, texts written in Arabizi are often disregarded in sentiment analysis tasks for Arabic.
Specializing Distributional Vectors of All Words for Lexical Entailment
Semantic specialization methods fine-tune distributional word vectors using lexical knowledge from external resources (e. g. WordNet) to accentuate a particular relation between words.
Generalized Tuning of Distributional Word Vectors for Monolingual and Cross-Lingual Lexical Entailment
Lexical entailment (LE; also known as hyponymy-hypernymy or is-a relation) is a core asymmetric lexical relation that supports tasks like taxonomy induction and text generation.
Computational Analysis of Political Texts: Bridging Research Efforts Across Communities
Political scientists created resources and used available NLP methods to process textual data largely in isolation from the NLP community.
Multilingual and Cross-Lingual Graded Lexical Entailment
Starting from HyperLex, the only available GR-LE dataset in English, we construct new monolingual GR-LE datasets for three other languages, and combine those to create a set of six cross-lingual GR-LE datasets termed CL-HYPERLEX.
ArguminSci: A Tool for Analyzing Argumentation and Rhetorical Aspects in Scientific Writing
Argumentation is arguably one of the central features of scientific language.
An Argument-Annotated Corpus of Scientific Publications
We analyze the annotated argumentative structures and investigate the relations between argumentation and other rhetorical aspects of scientific writing, such as discourse roles and citation contexts.
Investigating the Role of Argumentation in the Rhetorical Analysis of Scientific Publications with Neural Multi-Task Learning Models
Exponential growth in the number of scientific publications yields the need for effective automatic analysis of rhetorical aspects of scientific writing.
Explicit Retrofitting of Distributional Word Vectors
The ER model allows us to learn a global specialization function and specialize the vectors of words unobserved in the training data as well.
Discriminating between Lexico-Semantic Relations with the Specialization Tensor Model
We present a simple and effective feed-forward neural architecture for discriminating between lexico-semantic relations (synonymy, antonymy, hypernymy, and meronymy).
Predicting News Values from Headline Text and Emotions
We present a preliminary study on predicting news values from headline text and emotions.
Dual Tensor Model for Detecting Asymmetric Lexico-Semantic Relations
Detection of lexico-semantic relations is one of the central tasks of computational semantics.
Cross-Lingual Classification of Topics in Political Texts
In this paper, we propose an approach for cross-lingual topical coding of sentences from electoral manifestos of political parties in different languages.
Two Layers of Annotation for Representing Event Mentions in News Stories
In this paper, we describe our preliminary study on annotating event mention as a part of our research on high-precision news event extraction models.
Improving Neural Knowledge Base Completion with Cross-Lingual Projections
We exploit multilingual synsets from BabelNet to translate English triples to other languages and then augment the reference knowledge base with cross-lingual triples.
Unsupervised Cross-Lingual Scaling of Political Texts
Political text scaling aims to linearly order parties and politicians across political dimensions (e. g., left-to-right ideology) based on textual content (e. g., politician speeches or party manifestos).
HiEve: A Corpus for Extracting Event Hierarchies from News Stories
In this work, we present HiEve, a corpus for recognizing relations of spatiotemporal containment between events.
