Search Results for author:
Found 128 papers, 76 papers with code
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.
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.
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.
Natural Language Processing for Multilingual Task-Oriented Dialogue
In this tutorial, we will thus discuss and demonstrate the importance of (building) multilingual ToD systems, and then provide a systematic overview of current research gaps, challenges and initiatives related to multilingual ToD systems, with a particular focus on their connections to current research and challenges in multilingual and low-resource NLP.
Retrofitting (Large) Language Models with Dynamic Tokenization
To address these issues, we propose retrofitting LMs with dynamic tokenization: a way to dynamically decide on token boundaries based on the input text.
Aligning with Logic: Measuring, Evaluating and Improving Logical Consistency in Large Language Models
In this work, we focus on studying logical consistency of LLMs as a prerequisite for more reliable and trustworthy systems.
DARE: Diverse Visual Question Answering with Robustness Evaluation
Vision Language Models (VLMs) extend remarkable capabilities of text-only large language models and vision-only models, and are able to learn from and process multi-modal vision-text input.
Segment Any Text: A Universal Approach for Robust, Efficient and Adaptable Sentence Segmentation
We introduce a new model - Segment any Text (SaT) - to solve this problem.
Self-Distillation for Model Stacking Unlocks Cross-Lingual NLU in 200+ Languages
MT encoders, however, lack the knowledge necessary for comprehensive NLU that LLMs obtain through language modeling training on immense corpora.
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.
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.
Specialising and Analysing Instruction-Tuned and Byte-Level Language Models for Organic Reaction Prediction
This suggests that GPU-intensive and expensive pretraining on a large dataset of unlabelled molecules may be useful yet not essential to leverage the power of language models for chemistry.
Zero-Shot Tokenizer Transfer
Finally, we show that a ZeTT hypernetwork trained for a base (L)LM can also be applied to fine-tuned variants without extra training.
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
UNDIAL: Self-Distillation with Adjusted Logits for Robust Unlearning in Large Language Models
Mitigating the retention of sensitive or private information in large language models is essential for enhancing privacy and safety.
Natural Language Understanding
parameter-efficient fine-tuning
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.
Pheme: Efficient and Conversational Speech Generation
However, certain applications, such as assistive conversational systems, require natural and conversational speech generation tools that also operate efficiently in real 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.
Adapters: A Unified Library for Parameter-Efficient and Modular Transfer Learning
We introduce Adapters, an open-source library that unifies parameter-efficient and modular transfer learning in large language models.
$\textit{Dial BeInfo for Faithfulness}$: Improving Factuality of Information-Seeking Dialogue via Behavioural Fine-Tuning
Factuality is a crucial requirement in information seeking dialogue: the system should respond to the user's queries so that the responses are meaningful and aligned with the knowledge provided to the system.
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).
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
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.
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.
One For All & All For One: Bypassing Hyperparameter Tuning with Model Averaging For Cross-Lingual Transfer
Because of this, model selection based on source-language validation is unreliable: it picks model snapshots with suboptimal target-language performance.
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.
Knowledge-Aware Audio-Grounded Generative Slot Filling for Limited Annotated Data
In this work, we propose a Knowledge-Aware Audio-Grounded generative slot-filling framework, termed KA2G, that focuses on few-shot and zero-shot slot filling for ToD with speech input.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+6
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".
Where's the Point? Self-Supervised Multilingual Punctuation-Agnostic Sentence Segmentation
Many NLP pipelines split text into sentences as one of the crucial preprocessing steps.
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
Free Lunch: Robust Cross-Lingual Transfer via Model Checkpoint Averaging
The results indicate that averaging model checkpoints yields systematic and consistent performance gains across diverse target languages in all tasks.
CompoundPiece: Evaluating and Improving Decompounding Performance of Language Models
We first address the data gap by introducing a dataset of 255k compound and non-compound words across 56 diverse languages obtained from Wiktionary.
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.
Transfer to a Low-Resource Language via Close Relatives: The Case Study on Faroese
Multilingual language models have pushed state-of-the-art in cross-lingual NLP transfer.
Romanization-based Large-scale Adaptation of Multilingual Language Models
In order to boost the capacity of mPLMs to deal with low-resource and unseen languages, we explore the potential of leveraging transliteration on a massive scale.
Modular Deep Learning
Modular deep learning has emerged as a promising solution to these challenges.
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.
FUN with Fisher: Improving Generalization of Adapter-Based Cross-lingual Transfer with Scheduled Unfreezing
Our experiments reveal that scheduled unfreezing induces different learning dynamics compared to standard fine-tuning, and provide evidence that the dynamics of Fisher Information during training correlate with cross-lingual generalization performance.
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.
SLICER: Sliced Fine-Tuning for Low-Resource Cross-Lingual Transfer for Named Entity Recognition
Large multilingual language models generally demonstrate impressive results in zero-shot cross-lingual transfer, yet often fail to successfully transfer to low-resource languages, even for token-level prediction tasks like named entity recognition (NER).
Multilingual text classification
named-entity-recognition
+3
Square One Bias in NLP: Towards a Multi-Dimensional Exploration of the Research Manifold
Most work targeting multilinguality, for example, considers only accuracy; most work on fairness or interpretability considers only English; and so on.
Multi2WOZ: A Robust Multilingual Dataset and Conversational Pretraining for Task-Oriented Dialog
We then introduce a new framework for multilingual conversational specialization of pretrained language models (PrLMs) that aims to facilitate cross-lingual transfer for arbitrary downstream TOD 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.
EVI: Multilingual Spoken Dialogue Tasks and Dataset for Knowledge-Based Enrolment, Verification, and Identification
Knowledge-based authentication is crucial for task-oriented spoken dialogue systems that offer personalised and privacy-focused services.
Ranked #1 on
Speaker Identification
on EVI fr-FR
NLU++: A Multi-Label, Slot-Rich, Generalisable Dataset for Natural Language Understanding in Task-Oriented Dialogue
2) The ontology is divided into domain-specific and generic (i. e., domain-universal) intent modules that overlap across domains, promoting cross-domain reusability of annotated examples.
Improved and Efficient Conversational Slot Labeling through Question Answering
In particular, we focus on modeling and studying \textit{slot labeling} (SL), a crucial component of NLU for dialog, through the QA optics, aiming to improve both its performance and efficiency, and make it more effective and resilient to working with limited task data.
Parameter-Efficient Neural Reranking for Cross-Lingual and Multilingual Retrieval
Current approaches therefore commonly transfer rankers trained on English data to other languages and cross-lingual setups by means of multilingual encoders: they fine-tune all parameters of pretrained massively multilingual Transformers (MMTs, e. g., multilingual BERT) on English relevance judgments, and then deploy them in the target language(s).
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.
IGLUE: A Benchmark for Transfer Learning across Modalities, Tasks, and Languages
Our benchmark enables the evaluation of multilingual multimodal models for transfer learning, not only in a zero-shot setting, but also in newly defined few-shot learning setups.
On Cross-Lingual Retrieval with Multilingual Text Encoders
In this work we present a systematic empirical study focused on the suitability of the state-of-the-art multilingual encoders for cross-lingual document and sentence retrieval tasks across a number of diverse language pairs.
Prix-LM: Pretraining for Multilingual Knowledge Base Construction
To achieve this, it is crucial to represent multilingual knowledge in a shared/unified space.
Composable Sparse Fine-Tuning for Cross-Lingual Transfer
Both these masks can then be composed with the pretrained model.
ConvFiT: Conversational Fine-Tuning of Pretrained Language Models
Transformer-based language models (LMs) pretrained on large text collections are proven to store a wealth of semantic knowledge.
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.
xGQA: Cross-Lingual Visual Question Answering
In this work, we address this gap and provide xGQA, a new multilingual evaluation benchmark for the visual question answering task.
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.
Modelling Latent Translations for Cross-Lingual Transfer
To remedy this, we propose a new technique that integrates both steps of the traditional pipeline (translation and classification) into a single model, by treating the intermediate translations as a latent random variable.
RedditBias: A Real-World Resource for Bias Evaluation and Debiasing of Conversational Language Models
We use the evaluation framework to benchmark the widely used conversational DialoGPT model along with the adaptations of four debiasing methods.
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.
Multilingual and Cross-Lingual Intent Detection from Spoken Data
We present a systematic study on multilingual and cross-lingual intent detection from spoken data.
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
Retrieve Fast, Rerank Smart: Cooperative and Joint Approaches for Improved Cross-Modal Retrieval
Current state-of-the-art approaches to cross-modal retrieval process text and visual input jointly, relying on Transformer-based architectures with cross-attention mechanisms that attend over all words and objects in an image.
Evaluating Multilingual Text Encoders for Unsupervised Cross-Lingual Retrieval
Therefore, in this work we present a systematic empirical study focused on the suitability of the state-of-the-art multilingual encoders for cross-lingual document and sentence retrieval tasks across a large number of language pairs.
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.
UNKs Everywhere: Adapting Multilingual Language Models to New Scripts
The ultimate challenge is dealing with under-resourced languages not covered at all by the models and written in scripts unseen during pretraining.
How Good is Your Tokenizer? On the Monolingual Performance of Multilingual Language Models
In this work, we provide a systematic and comprehensive empirical comparison of pretrained multilingual language models versus their monolingual counterparts with regard to their monolingual task performance.
Orthogonal Language and Task Adapters in Zero-Shot Cross-Lingual Transfer
Adapter modules, additional trainable parameters that enable efficient fine-tuning of pretrained transformers, have recently been used for language specialization of multilingual transformers, improving downstream zero-shot cross-lingual transfer.
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.
ConVEx: Data-Efficient and Few-Shot Slot Labeling
We propose ConVEx (Conversational Value Extractor), an efficient pretraining and fine-tuning neural approach for slot-labeling dialog tasks.
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.
Is Supervised Syntactic Parsing Beneficial for Language Understanding? An Empirical Investigation
Traditional NLP has long held (supervised) syntactic parsing necessary for successful higher-level semantic language understanding (LU).
AdapterHub: A Framework for Adapting Transformers
We propose AdapterHub, a framework that allows dynamic "stitching-in" of pre-trained adapters for different tasks and languages.
Span-ConveRT: Few-shot Span Extraction for Dialog with Pretrained Conversational Representations
We introduce Span-ConveRT, a light-weight model for dialog slot-filling which frames the task as a turn-based span extraction task.
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)
From Zero to Hero: On the Limitations of Zero-Shot Cross-Lingual Transfer with Multilingual Transformers
Massively multilingual transformers pretrained with language modeling objectives (e. g., mBERT, XLM-R) have become a de facto default transfer paradigm for zero-shot cross-lingual transfer in NLP, offering unmatched transfer performance.
MAD-X: An Adapter-Based Framework for Multi-Task Cross-Lingual Transfer
The main goal behind state-of-the-art pre-trained multilingual models such as multilingual BERT and XLM-R is enabling and bootstrapping NLP applications in low-resource languages through zero-shot or few-shot cross-lingual transfer.
Ranked #5 on
Cross-Lingual Transfer
on XCOPA
(using extra training data)
Towards Instance-Level Parser Selection for Cross-Lingual Transfer of Dependency Parsers
Current methods of cross-lingual parser transfer focus on predicting the best parser for a low-resource target language globally, that is, "at treebank level".
Are All Good Word Vector Spaces Isomorphic?
Existing algorithms for aligning cross-lingual word vector spaces assume that vector spaces are approximately isomorphic.
Efficient Intent Detection with Dual Sentence Encoders
Building conversational systems in new domains and with added functionality requires resource-efficient models that work under low-data regimes (i. e., in few-shot setups).
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).
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.
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.
ConveRT: Efficient and Accurate Conversational Representations from Transformers
General-purpose pretrained sentence encoders such as BERT are not ideal for real-world conversational AI applications; they are computationally heavy, slow, and expensive to train.
Ranked #1 on
Conversational Response Selection
on PolyAI Reddit
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.
A General Framework for Implicit and Explicit Debiasing of Distributional Word Vector Spaces
Moreover, we successfully transfer debiasing models, by means of cross-lingual embedding spaces, and remove or attenuate biases in distributional word vector spaces of languages that lack readily available bias specifications.
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).
PolyResponse: A Rank-based Approach to Task-Oriented Dialogue with Application in Restaurant Search and Booking
We present PolyResponse, a conversational search engine that supports task-oriented dialogue.
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.
Training Neural Response Selection for Task-Oriented Dialogue Systems
Despite their popularity in the chatbot literature, retrieval-based models have had modest impact on task-oriented dialogue systems, with the main obstacle to their application being the low-data regime of most task-oriented dialogue tasks.
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.
A Repository of Conversational Datasets
Progress in Machine Learning is often driven by the availability of large datasets, and consistent evaluation metrics for comparing modeling approaches.
BIG-bench Machine Learning
Conversational Response Selection
+1
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.
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).
Scoring Lexical Entailment with a Supervised Directional Similarity Network
Experiments show excellent performance on scoring graded lexical entailment, raising the state-of-the-art on the HyperLex dataset by approximately 25%.
On the Limitations of Unsupervised Bilingual Dictionary Induction
Unsupervised machine translation---i. e., not assuming any cross-lingual supervision signal, whether a dictionary, translations, or comparable corpora---seems impossible, but nevertheless, Lample et al. (2018) recently proposed a fully unsupervised machine translation (MT) model.
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.
Unsupervised Cross-Lingual Information Retrieval using Monolingual Data Only
We propose a fully unsupervised framework for ad-hoc cross-lingual information retrieval (CLIR) which requires no bilingual data at all.
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.
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.
A Survey Of Cross-lingual Word Embedding Models
Cross-lingual representations of words enable us to reason about word meaning in multilingual contexts and are a key facilitator of cross-lingual transfer when developing natural language processing models for low-resource languages.
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.
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.
Decoding Sentiment from Distributed Representations of Sentences
Distributed representations of sentences have been developed recently to represent their meaning as real-valued vectors.
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.
Bilingual Distributed Word Representations from Document-Aligned Comparable Data
We propose a new model for learning bilingual word representations from non-parallel document-aligned data.
