Search Results for author:
Found 143 papers, 54 papers with code
Improving Machine Translation of Rare and Unseen Word Senses
The performance of NMT systems has improved drastically in the past few years but the translation of multi-sense words still poses a challenge.
MAD-G: Multilingual Adapter Generation for Efficient Cross-Lingual Transfer
While offering (1) improved fine-tuning efficiency (by a factor of around 50 in our experiments), (2) a smaller parameter budget, and (3) increased language coverage, MAD-G remains competitive with more expensive methods for language-specific adapter training across the board.
BAD-X: Bilingual Adapters Improve Zero-Shot Cross-Lingual Transfer
Adapter modules enable modular and efficient zero-shot cross-lingual transfer, where current state-of-the-art adapter-based approaches learn specialized language adapters (LAs) for individual languages.
Data Augmentation and Learned Layer Aggregation for Improved Multilingual Language Understanding in Dialogue
Scaling dialogue systems to a multitude of domains, tasks and languages relies on costly and time-consuming data annotation for different domain-task-language configurations.
Multi-SimLex: A Large-Scale Evaluation of Multilingual and Crosslingual Lexical Semantic Similarity
We introduce Multi-SimLex, a large-scale lexical resource and evaluation benchmark covering data sets for 12 typologically diverse languages, including major languages (e. g., Mandarin Chinese, Spanish, Russian) as well as less-resourced ones (e. g., Welsh, Kiswahili).
Semantic Data Set Construction from Human Clustering and Spatial Arrangement
We demonstrate how the resultant data set can be used for fine-grained analyses and evaluation of representation learning models on the intrinsic tasks of semantic clustering and semantic similarity.
Towards Better Context-aware Lexical Semantics:Adjusting Contextualized Representations through Static Anchors
One of the most powerful features of contextualized models is their dynamic embeddings for words in context, leading to state-of-the-art representations for context-aware lexical semantics.
A Rose by Any Other Name: LLM-Generated Explanations Are Good Proxies for Human Explanations to Collect Label Distributions on NLI
Disagreement in human labeling is ubiquitous, and can be captured in human judgment distributions (HJDs).
How far can bias go? -- Tracing bias from pretraining data to alignment
Our findings reveal that biases present in pre-training data are amplified in model outputs.
MURI: High-Quality Instruction Tuning Datasets for Low-Resource Languages via Reverse Instructions
Instruction tuning enhances large language models (LLMs) by aligning them with human preferences across diverse tasks.
SYNTHEVAL: Hybrid Behavioral Testing of NLP Models with Synthetic CheckLists
In this work, we propose SYNTHEVAL, a hybrid behavioral testing framework that leverages large language models (LLMs) to generate a wide range of test types for a comprehensive evaluation of NLP models.
Can Rule-Based Insights Enhance LLMs for Radiology Report Classification? Introducing the RadPrompt Methodology
Developing imaging models capable of detecting pathologies from chest X-rays can be cost and time-prohibitive for large datasets as it requires supervision to attain state-of-the-art performance.
TurkishMMLU: Measuring Massive Multitask Language Understanding in Turkish
These questions are written by curriculum experts, suitable for the high-school curricula in Turkey, covering subjects ranging from natural sciences and math questions to more culturally representative topics such as Turkish Literature and the history of the Turkish Republic.
"Seeing the Big through the Small": Can LLMs Approximate Human Judgment Distributions on NLI from a Few Explanations?
This study proposes to exploit LLMs to approximate HJDs using a small number of expert labels and explanations.
Fairer Preferences Elicit Improved Human-Aligned Large Language Model Judgments
Motivated by this phenomenon, we propose an automatic Zero-shot Evaluation-oriented Prompt Optimization framework, ZEPO, which aims to produce fairer preference decisions and improve the alignment of LLM evaluators with human judgments.
Culturally Aware and Adapted NLP: A Taxonomy and a Survey of the State of the Art
The surge of interest in culturally aware and adapted Natural Language Processing (NLP) has inspired much recent research.
TopViewRS: Vision-Language Models as Top-View Spatial Reasoners
In this work, we thus study their capability to understand and reason over spatial relations from the top view.
Spectral Editing of Activations for Large Language Model Alignment
Large language models (LLMs) often exhibit undesirable behaviours, such as generating untruthful or biased content.
CALRec: Contrastive Alignment of Generative LLMs for Sequential Recommendation
Building upon the success of Large Language Models (LLMs) in a variety of tasks, researchers have recently explored using LLMs that are pretrained on vast corpora of text for sequential recommendation.
Aligning with Human Judgement: The Role of Pairwise Preference in Large Language Model Evaluators
Large Language Models (LLMs) have demonstrated promising capabilities as automatic evaluators in assessing the quality of generated natural language.
Analyzing and Adapting Large Language Models for Few-Shot Multilingual NLU: Are We There Yet?
Supervised fine-tuning (SFT), supervised instruction tuning (SIT) and in-context learning (ICL) are three alternative, de facto standard approaches to few-shot learning.
Self-Augmented In-Context Learning for Unsupervised Word Translation
Recent work has shown that, while large language models (LLMs) demonstrate strong word translation or bilingual lexicon induction (BLI) capabilities in few-shot setups, they still cannot match the performance of 'traditional' mapping-based approaches in the unsupervised scenario where no seed translation pairs are available, especially for lower-resource languages.
Bilingual Lexicon Induction
Cross-Lingual Word Embeddings
+9
Scaling Sparse Fine-Tuning to Large Language Models
We experiment with instruction-tuning of LLMs on standard dataset mixtures, finding that SpIEL is often superior to popular parameter-efficient fine-tuning methods like LoRA (low-rank adaptation) in terms of performance and comparable in terms of run time.
DIALIGHT: Lightweight Multilingual Development and Evaluation of Task-Oriented Dialogue Systems with Large Language Models
We present DIALIGHT, a toolkit for developing and evaluating multilingual Task-Oriented Dialogue (ToD) systems which facilitates systematic evaluations and comparisons between ToD systems using fine-tuning of Pretrained Language Models (PLMs) and those utilising the zero-shot and in-context learning capabilities of Large Language Models (LLMs).
On Task Performance and Model Calibration with Supervised and Self-Ensembled In-Context Learning
Following the standard supervised fine-tuning (SFT) paradigm, in-context learning (ICL) has become an efficient approach propelled by the recent advancements in large language models (LLMs), yielding promising performance across various tasks in few-shot data setups.
SQATIN: Supervised Instruction Tuning Meets Question Answering for Improved Dialogue NLU
Task-oriented dialogue (ToD) systems help users execute well-defined tasks across a variety of domains (e. g., $\textit{flight booking}$ or $\textit{food ordering}$), with their Natural Language Understanding (NLU) components being dedicated to the analysis of user utterances, predicting users' intents ($\textit{Intent Detection}$, ID) and extracting values for informational slots ($\textit{Value Extraction}$, VE).
Are Large Language Models Temporally Grounded?
Instead, we provide LLMs with textual narratives and probe them with respect to their common-sense knowledge of the structure and duration of events, their ability to order events along a timeline, and self-consistency within their temporal model (e. g., temporal relations such as after and before are mutually exclusive for any pair of events).
Quantifying the Dialect Gap and its Correlates Across Languages
Historically, researchers and consumers have noticed a decrease in quality when applying NLP tools to minority variants of languages (i. e. Puerto Rican Spanish or Swiss German), but studies exploring this have been limited to a select few languages.
On Bilingual Lexicon Induction with Large Language Models
Bilingual Lexicon Induction (BLI) is a core task in multilingual NLP that still, to a large extent, relies on calculating cross-lingual word representations.
Bilingual Lexicon Induction
Cross-Lingual Word Embeddings
+8
A Systematic Study of Performance Disparities in Multilingual Task-Oriented Dialogue Systems
Achieving robust language technologies that can perform well across the world's many languages is a central goal of multilingual NLP.
Survival of the Most Influential Prompts: Efficient Black-Box Prompt Search via Clustering and Pruning
Prompt-based learning has been an effective paradigm for large pretrained language models (LLM), enabling few-shot or even zero-shot learning.
Navigating Cultural Chasms: Exploring and Unlocking the Cultural POV of Text-To-Image Models
Text-To-Image (TTI) models, such as DALL-E and StableDiffusion, have demonstrated remarkable prompt-based image generation capabilities.
Multi3WOZ: A Multilingual, Multi-Domain, Multi-Parallel Dataset for Training and Evaluating Culturally Adapted Task-Oriented Dialog Systems
Creating high-quality annotated data for task-oriented dialog (ToD) is known to be notoriously difficult, and the challenges are amplified when the goal is to create equitable, culturally adapted, and large-scale ToD datasets for multiple languages.
Cross-Lingual Transfer with Target Language-Ready Task Adapters
We address this mismatch by exposing the task adapter to the target language adapter during training, and empirically validate several variants of the idea: in the simplest form, we alternate between using the source and target language adapters during task adapter training, which can be generalized to cycling over any set of language adapters.
Distilling Efficient Language-Specific Models for Cross-Lingual Transfer
Specifically, we use a two-phase distillation approach, termed BiStil: (i) the first phase distils a general bilingual model from the MMT, while (ii) the second, task-specific phase sparsely fine-tunes the bilingual "student" model using a task-tuned variant of the original MMT as its "teacher".
Translation-Enhanced Multilingual Text-to-Image Generation
Research on text-to-image generation (TTI) still predominantly focuses on the English language due to the lack of annotated image-caption data in other languages; in the long run, this might widen inequitable access to TTI technology.
Cross-lingual Text-to-Image Generation
Crosslingual Text-to-Image Generation
+6
Detecting and Mitigating Hallucinations in Multilingual Summarisation
With the existing faithful metrics focusing on English, even measuring the extent of this phenomenon in cross-lingual settings is hard.
Language-Agnostic Bias Detection in Language Models with Bias Probing
For nationality as a case study, we show that LABDet `surfaces' nationality bias by training a classifier on top of a frozen PLM on non-nationality sentiment detection.
Transfer-Free Data-Efficient Multilingual Slot Labeling
It is especially effective for the most challenging transfer-free few-shot setups, paving the way for quick and data-efficient bootstrapping of multilingual slot labelers for ToD.
LongForm: Effective Instruction Tuning with Reverse Instructions
We generate instructions via LLMs for human-written corpus examples using reverse instructions.
AutoPEFT: Automatic Configuration Search for Parameter-Efficient Fine-Tuning
Large pretrained language models are widely used in downstream NLP tasks via task-specific fine-tuning, but such procedures can be costly.
MULTI3NLU++: A Multilingual, Multi-Intent, Multi-Domain Dataset for Natural Language Understanding in Task-Oriented Dialogue
We use MULTI3NLU++ to benchmark state-of-the-art multilingual models for the NLU tasks of intent detection and slot labelling for TOD systems in the multilingual setting.
Reranking Overgenerated Responses for End-to-End Task-Oriented Dialogue Systems
At training, the high-scoring partition comprises all generated responses whose similarity to the gold response is higher than the similarity of the greedy response to the gold response.
Improving Bilingual Lexicon Induction with Cross-Encoder Reranking
This crucial step is done via 1) creating a word similarity dataset, comprising positive word pairs (i. e., true translations) and hard negative pairs induced from the original CLWE space, and then 2) fine-tuning an mPLM (e. g., mBERT or XLM-R) in a cross-encoder manner to predict the similarity scores.
Bilingual Lexicon Induction
Cross-Lingual Word Embeddings
+7
Can Pretrained Language Models (Yet) Reason Deductively?
Acquiring factual knowledge with Pretrained Language Models (PLMs) has attracted increasing attention, showing promising performance in many knowledge-intensive tasks.
Probing Cross-Lingual Lexical Knowledge from Multilingual Sentence Encoders
In this work, we probe SEs for the amount of cross-lingual lexical knowledge stored in their parameters, and compare them against the original multilingual LMs.
Improving Word Translation via Two-Stage Contrastive Learning
At Stage C1, we propose to refine standard cross-lingual linear maps between static word embeddings (WEs) via a contrastive learning objective; we also show how to integrate it into the self-learning procedure for even more refined cross-lingual maps.
Delving Deeper into Cross-lingual Visual Question Answering
2) We analyze cross-lingual VQA across different question types of varying complexity for different multilingual multimodal Transformers, and identify question types that are the most difficult to improve on.
Cross-Lingual Dialogue Dataset Creation via Outline-Based Generation
Through this process we annotate a new large-scale dataset for training and evaluation of multilingual and cross-lingual ToD systems.
Measuring Context-Word Biases in Lexical Semantic Datasets
Our findings demonstrate that models are usually not being tested for word-in-context semantics in the same way as humans are in these tasks, which helps us better understand the model-human gap.
Composable Sparse Fine-Tuning for Cross-Lingual Transfer
Both these masks can then be composed with the pretrained model.
MirrorWiC: On Eliciting Word-in-Context Representations from Pretrained Language Models
Recent work indicated that pretrained language models (PLMs) such as BERT and RoBERTa can be transformed into effective sentence and word encoders even via simple self-supervised techniques.
Towards Zero-shot Language Modeling
Motivated by this question, we aim at constructing an informative prior over neural weights, in order to adapt quickly to held-out languages in the task of character-level language modeling.
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).
Learning Domain-Specialised Representations for Cross-Lingual Biomedical Entity Linking
To this end, we propose and evaluate a series of cross-lingual transfer methods for the XL-BEL task, and demonstrate that general-domain bitext helps propagate the available English knowledge to languages with little to no in-domain data.
Crossing the Conversational Chasm: A Primer on Natural Language Processing for Multilingual Task-Oriented Dialogue Systems
We find that the most critical factor preventing the creation of truly multilingual ToD systems is the lack of datasets in most languages for both training and evaluation.
AM2iCo: Evaluating Word Meaning in Context across Low-Resource Languages with Adversarial Examples
In order to address these gaps, we present AM2iCo (Adversarial and Multilingual Meaning in Context), a wide-coverage cross-lingual and multilingual evaluation set; it aims to faithfully assess the ability of state-of-the-art (SotA) representation models to understand the identity of word meaning in cross-lingual contexts for 14 language pairs.
Fast, Effective, and Self-Supervised: Transforming Masked Language Models into Universal Lexical and Sentence Encoders
In this work, we demonstrate that it is possible to turn MLMs into effective universal lexical and sentence encoders even without any additional data and without any supervision.
Ranked #15 on
Semantic Textual Similarity
on STS16
Contrastive Learning
Cross-Lingual Semantic Textual Similarity
+5
Combining Deep Generative Models and Multi-lingual Pretraining for Semi-supervised Document Classification
Semi-supervised learning through deep generative models and multi-lingual pretraining techniques have orchestrated tremendous success across different areas of NLP.
Verb Knowledge Injection for Multilingual Event Processing
We investigate whether injecting explicit information on verbs' semantic-syntactic behaviour improves the performance of LM-pretrained Transformers in event extraction tasks -- downstream tasks for which accurate verb processing is paramount.
A Closer Look at Few-Shot Crosslingual Transfer: The Choice of Shots Matters
Few-shot crosslingual transfer has been shown to outperform its zero-shot counterpart with pretrained encoders like multilingual BERT.
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.
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).
Emergent Communication Pretraining for Few-Shot Machine Translation
On the other hand, this also provides an extrinsic evaluation protocol to probe the properties of emergent languages ex vitro.
Probing Pretrained Language Models for Lexical Semantics
The success of large pretrained language models (LMs) such as BERT and RoBERTa has sparked interest in probing their representations, in order to unveil what types of knowledge they implicitly capture.
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.
Investigating Word-Class Distributions in Word Vector Spaces
This paper presents an investigation on the distribution of word vectors belonging to a certain word class in a pre-trained word vector space.
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.
Multidirectional Associative Optimization of Function-Specific Word Representations
We present a neural framework for learning associations between interrelated groups of words such as the ones found in Subject-Verb-Object (SVO) structures.
XCOPA: A Multilingual Dataset for Causal Commonsense Reasoning
In order to simulate human language capacity, natural language processing systems must be able to reason about the dynamics of everyday situations, including their possible causes and effects.
Ranked #3 on
Cross-Lingual Transfer
on XCOPA
(using extra training data)
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.
Stylistic Dialogue Generation via Information-Guided Reinforcement Learning Strategy
To enable better balance between the content quality and the style, we introduce a new training strategy, know as Information-Guided Reinforcement Learning (IG-RL).
Prototype-to-Style: Dialogue Generation with Style-Aware Editing on Retrieval Memory
A stylistic response generator then takes the prototype and the desired language style as model input to obtain a high-quality and stylistic response.
Multi-SimLex: A Large-Scale Evaluation of Multilingual and Cross-Lingual Lexical Semantic Similarity
We introduce Multi-SimLex, a large-scale lexical resource and evaluation benchmark covering datasets for 12 typologically diverse languages, including major languages (e. g., Mandarin Chinese, Spanish, Russian) as well as less-resourced ones (e. g., Welsh, Kiswahili).
The Secret is in the Spectra: Predicting Cross-lingual Task Performance with Spectral Similarity Measures
Performance in cross-lingual NLP tasks is impacted by the (dis)similarity of languages at hand: e. g., previous work has suggested there is a connection between the expected success of bilingual lexicon induction (BLI) and the assumption of (approximate) isomorphism between monolingual embedding spaces.
Parameter Space Factorization for Zero-Shot Learning across Tasks and Languages
In this work, we propose a Bayesian generative model for the space of neural parameters.
Semi-supervised Bootstrapping of Dialogue State Trackers for Task Oriented Modelling
Dialogue systems benefit greatly from optimizing on detailed annotations, such as transcribed utterances, internal dialogue state representations and dialogue act labels.
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.
Semi-Supervised Bootstrapping of Dialogue State Trackers for Task-Oriented Modelling
Dialogue systems benefit greatly from optimizing on detailed annotations, such as transcribed utterances, internal dialogue state representations and dialogue act labels.
On the Importance of Subword Information for Morphological Tasks in Truly Low-Resource Languages
Recent work has validated the importance of subword information for word representation learning.
Specializing Unsupervised Pretraining Models for Word-Level Semantic Similarity
In this work, we complement such distributional knowledge with external lexical knowledge, that is, we integrate the discrete knowledge on word-level semantic similarity into pretraining.
Do We Really Need Fully Unsupervised Cross-Lingual Embeddings?
A series of bilingual lexicon induction (BLI) experiments with 15 diverse languages (210 language pairs) show that fully unsupervised CLWE methods still fail for a large number of language pairs (e. g., they yield zero BLI performance for 87/210 pairs).
Enhancing biomedical word embeddings by retrofitting to verb clusters
Verbs play a fundamental role in many biomed-ical tasks and applications such as relation and event extraction.
Bayesian Learning for Neural Dependency Parsing
While neural dependency parsers provide state-of-the-art accuracy for several languages, they still rely on large amounts of costly labeled training data.
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.
Second-order contexts from lexical substitutes for few-shot learning of word representations
There is a growing awareness of the need to handle rare and unseen words in word representation modelling.
A Systematic Study of Leveraging Subword Information for Learning Word Representations
The use of subword-level information (e. g., characters, character n-grams, morphemes) has become ubiquitous in modern word representation learning.
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.
Adversarial Propagation and Zero-Shot Cross-Lingual Transfer of Word Vector Specialization
Our adversarial post-specialization method propagates the external lexical knowledge to the full distributional space.
Modeling Language Variation and Universals: A Survey on Typological Linguistics for Natural Language Processing
Linguistic typology aims to capture structural and semantic variation across the world's languages.
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.
Post-Specialisation: Retrofitting Vectors of Words Unseen in Lexical Resources
Word vector specialisation (also known as retrofitting) is a portable, light-weight approach to fine-tuning arbitrary distributional word vector spaces by injecting external knowledge from rich lexical resources such as WordNet.
Language Modeling for Morphologically Rich Languages: Character-Aware Modeling for Word-Level Prediction
Neural architectures are prominent in the construction of language models (LMs).
Erratum: Link prediction in drug-target interactions network using similarity indices
Conclusion: This demonstrates that approaches purely based on network topology provide a more suitable approach to DTI prediction in the many real-life situations where little or no prior knowledge is available about the characteristics of drugs, targets, or their interactions.
A neural network multi-task learning approach to biomedical named entity recognition
Additionally, we investigated the effect of dataset size on performance in both single- and multi-task settings.
Initializing neural networks for hierarchical multi-label text classification
Many tasks in the biomedical domain require the assignment of one or more predefined labels to input text, where the labels are a part of a hierarchical structure (such as a taxonomy).
Cross-Lingual Induction and Transfer of Verb Classes Based on Word Vector Space Specialisation
Existing approaches to automatic VerbNet-style verb classification are heavily dependent on feature engineering and therefore limited to languages with mature NLP pipelines.
Semantic Specialisation 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.
Morph-fitting: Fine-Tuning Word Vector Spaces with Simple Language-Specific Rules
Morphologically rich languages accentuate two properties of distributional vector space models: 1) the difficulty of inducing accurate representations for low-frequency word forms; and 2) insensitivity to distinct lexical relations that have similar distributional signatures.
Decoding Sentiment from Distributed Representations of Sentences
Distributed representations of sentences have been developed recently to represent their meaning as real-valued vectors.
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.
Event-Related Features in Feedforward Neural Networks Contribute to Identifying Causal Relations in Discourse
Causal relations play a key role in information extraction and reasoning.
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.
Robust Text Classification for Sparsely Labelled Data Using Multi-level Embeddings
The conventional solution for handling sparsely labelled data is extensive feature engineering.
Cancer Hallmark Text Classification Using Convolutional Neural Networks
Methods based on deep learning approaches have recently achieved state-of-the-art performance in a range of machine learning tasks and are increasingly applied to natural language processing (NLP).
Survey on the Use of Typological Information in Natural Language Processing
In recent years linguistic typology, which classifies the world's languages according to their functional and structural properties, has been widely used to support multilingual NLP.
Automatic Selection of Context Configurations for Improved Class-Specific Word Representations
With our selected context configurations, we train on only 14% (A), 26. 2% (V), and 33. 6% (N) of all dependency-based contexts, resulting in a reduced training time.
HyperLex: A Large-Scale Evaluation of Graded Lexical Entailment
We introduce HyperLex - a dataset and evaluation resource that quantifies the extent of of the semantic category membership, that is, type-of relation also known as hyponymy-hypernymy or lexical entailment (LE) relation between 2, 616 concept pairs.
SimVerb-3500: A Large-Scale Evaluation Set of Verb Similarity
Verbs play a critical role in the meaning of sentences, but these ubiquitous words have received little attention in recent distributional semantics research.
Anchoring and Agreement in Syntactic Annotations
Our agreement results control for parser bias, and are consequential in that they are on par with state of the art parsing performance for English newswire.
Learning Distributed Representations of Sentences from Unlabelled Data
Unsupervised methods for learning distributed representations of words are ubiquitous in today's NLP research, but far less is known about the best ways to learn distributed phrase or sentence representations from unlabelled data.
Ranked #16 on
Subjectivity Analysis
on SUBJ
Automatic semantic classification of scientific literature according to the hallmarks of cancer
The hallmarks of cancer have become highly influential in cancer research.
Learning to Understand Phrases by Embedding the Dictionary
Distributional models that learn rich semantic word representations are a success story of recent NLP research.
Unsupervised Declarative Knowledge Induction for Constraint-Based Learning of Information Structure in Scientific Documents
Inferring the information structure of scientific documents is useful for many NLP applications.
SimLex-999: Evaluating Semantic Models with (Genuine) Similarity Estimation
We present SimLex-999, a gold standard resource for evaluating distributional semantic models that improves on existing resources in several important ways.
Native Language Identification Using Large, Longitudinal Data
Native Language Identification (NLI) is a task aimed at determining the native language (L1) of learners of second language (L2) on the basis of their written texts.
BIG-bench Machine Learning
Native Language Identification
+1
Multi-Modal Models for Concrete and Abstract Concept Meaning
Multi-modal models that learn semantic representations from both linguistic and perceptual input outperform language-only models on a range of evaluations, and better reflect human concept acquisition.
