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Found 54 papers, 6 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 3: Graded Word Similarity in Context
This paper presents the Graded Word Similarity in Context (GWSC) task which asked participants to predict the effects of context on human perception of similarity in English, Croatian, Slovene and Finnish.
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.
Manual Clustering and Spatial Arrangement of Verbs for Multilingual Evaluation and Typology Analysis
We present the first evaluation of the applicability of a spatial arrangement method (SpAM) to a typologically diverse language sample, and its potential to produce semantic evaluation resources to support multilingual NLP, with a focus on verb semantics.
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.
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.
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
+2
Spatial Multi-Arrangement for Clustering and Multi-way Similarity Dataset Construction
We present a novel methodology for fast bottom-up creation of large-scale semantic similarity resources to support development and evaluation of NLP systems.
Investigating Cross-Lingual Alignment Methods for Contextualized Embeddings with Token-Level Evaluation
In this paper, we present a thorough investigation on methods that align pre-trained contextualized embeddings into shared cross-lingual context-aware embedding space, providing strong reference benchmarks for future context-aware crosslingual models.
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.
Hello, It's GPT-2 - How Can I Help You? Towards the Use of Pretrained Language Models for Task-Oriented Dialogue Systems
Data scarcity is a long-standing and crucial challenge that hinders quick development of task-oriented dialogue systems across multiple domains: task-oriented dialogue models are expected to learn grammar, syntax, dialogue reasoning, decision making, and language generation from absurdly small amounts of task-specific data.
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.
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.
Unsupervised Cross-Lingual Representation Learning
In this tutorial, we provide a comprehensive survey of the exciting recent work on cutting-edge weakly-supervised and unsupervised cross-lingual word representations.
JW300: A Wide-Coverage Parallel Corpus for Low-Resource Languages
Viable cross-lingual transfer critically depends on the availability of parallel texts.
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.
Show Some Love to Your n-grams: A Bit of Progress and Stronger n-gram Language Modeling Baselines
In recent years neural language models (LMs) have set the state-of-the-art performance for several benchmarking datasets.
Learning Unsupervised Multilingual Word Embeddings with Incremental Multilingual Hubs
We learn a shared multilingual embedding space for a variable number of languages by incrementally adding new languages one by one to the current multilingual space.
Bilingual Lexicon Induction
Cross-Lingual Word Embeddings
+4
On the Relation between Linguistic Typology and (Limitations of) Multilingual Language Modeling
A key challenge in cross-lingual NLP is developing general language-independent architectures that are equally applicable to any language.
Injecting Lexical Contrast into Word Vectors by Guiding Vector Space Specialisation
Word vector space specialisation models offer a portable, light-weight approach to fine-tuning arbitrary distributional vector spaces to discern between synonymy and antonymy.
Isomorphic Transfer of Syntactic Structures in Cross-Lingual NLP
The transfer or share of knowledge between languages is a potential solution to resource scarcity in NLP.
Bridging Languages through Images with Deep Partial Canonical Correlation Analysis
We present a deep neural network that leverages images to improve bilingual text embeddings.
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.
Fully Statistical Neural Belief Tracking
This paper proposes an improvement to the existing data-driven Neural Belief Tracking (NBT) framework for Dialogue State Tracking (DST).
Deep Learning for Conversational AI
The main purpose of this tutorial is to encourage dialogue research in the NLP community by providing the research background, a survey of available resources, and giving key insights to application of state-of-the-art SDS methodology into industry-scale conversational AI systems.
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).
Specialising Word Vectors for Lexical Entailment
We present LEAR (Lexical Entailment Attract-Repel), a novel post-processing method that transforms any input word vector space to emphasise the asymmetric relation of lexical entailment (LE), also known as the IS-A or hyponymy-hypernymy relation.
Ranked #1 on
Lexical Entailment
on HyperLex
Language Modeling for Morphologically Rich Languages: Character-Aware Modeling for Word-Level Prediction
Neural architectures are prominent in the construction of language models (LMs).
Cross-Lingual Word Representations: Induction and Evaluation
With the increasing use of monolingual word vectors, there is a need for word vectors that can be used as efficiently across multiple languages as monolingually.
Cross-Lingual Syntactically Informed Distributed Word Representations
We develop a novel cross-lingual word representation model which injects syntactic information through dependency-based contexts into a shared cross-lingual word vector space.
Evaluation by Association: A Systematic Study of Quantitative Word Association Evaluation
Recent work on evaluating representation learning architectures in NLP has established a need for evaluation protocols based on subconscious cognitive measures rather than manually tailored intrinsic similarity and relatedness tasks.
Word Vector Space Specialisation
Specialising vector spaces to maximise their content with respect to one key property of vector space models (e. g. semantic similarity vs. relatedness or lexical entailment) while mitigating others has become an active and attractive research topic in representation learning.
Bilingual Lexicon Induction by Learning to Combine Word-Level and Character-Level Representations
We study the problem of bilingual lexicon induction (BLI) in a setting where some translation resources are available, but unknown translations are sought for certain, possibly domain-specific terminology.
Semantic Specialization of Distributional Word Vector Spaces using Monolingual and Cross-Lingual Constraints
We present Attract-Repel, an algorithm for improving the semantic quality of word vectors by injecting constraints extracted from lexical resources.
TermWise: A CAT-tool with Context-Sensitive Terminological Support.
In this paper, we report on the TermWise project, a cooperation of terminologists, corpus linguists and computer scientists, that aims to leverage big online translation data for terminological support to legal translators at the Belgian Federal Ministry of Justice.
