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Found 131 papers, 46 papers with code
Findings of the WMT 2020 Shared Task on Chat Translation
We report the results of the first edition of the WMT shared task on chat translation.
Personal Information Leakage Detection in Conversations
In this work, we propose to protect personal information by warning users of detected suspicious sentences generated by conversational assistants.
Explaining Decision-Tree Predictions by Addressing Potential Conflicts between Predictions and Plausible Expectations
We offer an approach to explain Decision Tree (DT) predictions by addressing potential conflicts between aspects of these predictions and plausible expectations licensed by background information.
Domain Generalisation of NMT: Fusing Adapters with Leave-One-Domain-Out Training
Generalising to unseen domains is under-explored and remains a challenge in neural machine translation.
Document Level Hierarchical Transformer
In this paper, we present a novel semi-autoregressive document generation model capable of revising and editing the generated text.
Curriculum Learning Effectively Improves Low Data VQA
In this paper, we propose curriculum-based learning (CL) regime to increase the accuracy of VQA models trained on small datasets.
Multilingual Neural Machine Translation: Can Linguistic Hierarchies Help?
Multilingual Neural Machine Translation (MNMT) trains a single NMT model that supports translation between multiple languages, rather than training separate models for different languages.
Lifelong Explainer for Lifelong Learners
Our ablation study shows that the ER mechanism in our LLE approach enhances the learning capabilities of the student explainer.
Investigating Pre-trained Audio Encoders in the Low-Resource Condition
Pre-trained speech encoders have been central to pushing state-of-the-art results across various speech understanding and generation tasks.
FACTUAL: A Benchmark for Faithful and Consistent Textual Scene Graph Parsing
Textual scene graph parsing has become increasingly important in various vision-language applications, including image caption evaluation and image retrieval.
NormMark: A Weakly Supervised Markov Model for Socio-cultural Norm Discovery
Norms, which are culturally accepted guidelines for behaviours, can be integrated into conversational models to generate utterances that are appropriate for the socio-cultural context.
The Best of Both Worlds: Combining Human and Machine Translations for Multilingual Semantic Parsing with Active Learning
Multilingual semantic parsing aims to leverage the knowledge from the high-resource languages to improve low-resource semantic parsing, yet commonly suffers from the data imbalance problem.
A Minimal Approach for Natural Language Action Space in Text-based Games
Text-based games (TGs) are language-based interactive environments for reinforcement learning.
Active Continual Learning: Labelling Queries in a Sequence of Tasks
Acquiring new knowledge without forgetting what has been learned in a sequence of tasks is the central focus of continual learning (CL).
Turning Flowchart into Dialog: Plan-based Data Augmentation for Low-Resource Flowchart-grounded Troubleshooting Dialogs
Flowchart-grounded troubleshooting dialogue (FTD) systems, which follow the instructions of a flowchart to diagnose users' problems in specific domains (eg., vehicle, laptop), have been gaining research interest in recent years.
SocialDial: A Benchmark for Socially-Aware Dialogue Systems
To the best of our knowledge, SocialDial is the first socially-aware dialogue dataset that covers multiple social factors and has fine-grained labels.
Cultural Vocal Bursts Intensity Prediction
Synthetic Data Generation
Normalizing Flow-based Neural Process for Few-Shot Knowledge Graph Completion
In this paper, we propose a normalizing flow-based neural process for few-shot knowledge graph completion (NP-FKGC).
Koala: An Index for Quantifying Overlaps with Pre-training Corpora
In very recent years more attention has been placed on probing the role of pre-training data in Large Language Models (LLMs) downstream behaviour.
ProtoCon: Pseudo-label Refinement via Online Clustering and Prototypical Consistency for Efficient Semi-supervised Learning
Finally, ProtoCon addresses the poor training signal in the initial phase of training (due to fewer confident predictions) by introducing an auxiliary self-supervised loss.
Less is More: Mitigate Spurious Correlations for Open-Domain Dialogue Response Generation Models by Causal Discovery
In this paper, we conduct the first study on spurious correlations for open-domain response generation models based on a corpus CGDIALOG curated in our work.
Document Flattening: Beyond Concatenating Context for Document-Level Neural Machine Translation
Existing work in document-level neural machine translation commonly concatenates several consecutive sentences as a pseudo-document, and then learns inter-sentential dependencies.
On Robustness of Prompt-based Semantic Parsing with Large Pre-trained Language Model: An Empirical Study on Codex
Semantic parsing is a technique aimed at constructing a structured representation of the meaning of a natural-language question.
Active Learning for Multilingual Semantic Parser
Current multilingual semantic parsing (MSP) datasets are almost all collected by translating the utterances in the existing datasets from the resource-rich language to the target language.
Let's Negotiate! A Survey of Negotiation Dialogue Systems
Negotiation is one of the crucial abilities in human communication, and there has been a resurgent research interest in negotiation dialogue systems recently, which goal is to empower intelligent agents with such ability that can efficiently help humans resolve conflicts or reach beneficial agreements.
Learning Object-Language Alignments for Open-Vocabulary Object Detection
In this paper, we propose a novel open-vocabulary object detection framework directly learning from image-text pair data.
Complex Reading Comprehension Through Question Decomposition
Multi-hop reading comprehension requires not only the ability to reason over raw text but also the ability to combine multiple evidence.
Self-supervised Rewiring of Pre-trained Speech Encoders: Towards Faster Fine-tuning with Less Labels in Speech Processing
Pre-trained speech Transformers have facilitated great success across various speech processing tasks.
Can Domains Be Transferred Across Languages in Multi-Domain Multilingual Neural Machine Translation?
Previous works mostly focus on either multilingual or multi-domain aspects of neural machine translation (NMT).
LAVA: Label-efficient Visual Learning and Adaptation
We present LAVA, a simple yet effective method for multi-domain visual transfer learning with limited data.
Towards Relation Extraction From Speech
We explore speech relation extraction via two approaches: the pipeline approach conducting text-based extraction with a pretrained ASR module, and the end2end approach via a new proposed encoder-decoder model, or what we called SpeechRE.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
RedApt: An Adaptor for wav2vec 2 Encoding \\ Faster and Smaller Speech Translation without Quality Compromise
Pre-trained speech Transformers in speech translation (ST) have facilitated state-of-the-art (SotA) results; yet, using such encoders is computationally expensive.
Teaching Neural Module Networks to Do Arithmetic
Answering complex questions that require multi-step multi-type reasoning over raw text is challenging, especially when conducting numerical reasoning.
Feature-based Learning for Diverse and Privacy-Preserving Counterfactual Explanations
Interpretable machine learning seeks to understand the reasoning process of complex black-box systems that are long notorious for lack of explainability.
An Additive Instance-Wise Approach to Multi-class Model Interpretation
A popular attribution-based approach is to exploit local neighborhoods for learning instance-specific explainers in an additive manner.
M-Adapter: Modality Adaptation for End-to-End Speech-to-Text Translation
End-to-end speech-to-text translation models are often initialized with pre-trained speech encoder and pre-trained text decoder.
Variational Autoencoder with Disentanglement Priors for Low-Resource Task-Specific Natural Language Generation
In this paper, we propose a variational autoencoder with disentanglement priors, VAE-DPRIOR, for task-specific natural language generation with none or a handful of task-specific labeled examples.
Utilizing Wordnets for Cognate Detection among Indian Languages
Automatic Cognate Detection (ACD) is a challenging task which has been utilized to help NLP applications like Machine Translation, Information Retrieval and Computational Phylogenetics.
Challenge Dataset of Cognates and False Friend Pairs from Indian Languages
In this paper, we describe the creation of two cognate datasets for twelve Indian languages, namely Sanskrit, Hindi, Assamese, Oriya, Kannada, Gujarati, Tamil, Telugu, Punjabi, Bengali, Marathi, and Malayalam.
Harnessing Cross-lingual Features to Improve Cognate Detection for Low-resource Languages
We, then, evaluate the impact of our cognate detection mechanism on neural machine translation (NMT), as a downstream task.
Cross-Lingual Information Retrieval
Cross-Lingual Word Embeddings
+5
Cognition-aware Cognate Detection
We collect gaze behaviour data for a small sample of cognates and show that extracted cognitive features help the task of cognate detection.
BaLeNAS: Differentiable Architecture Search via the Bayesian Learning Rule
Differentiable Architecture Search (DARTS) has received massive attention in recent years, mainly because it significantly reduces the computational cost through weight sharing and continuous relaxation.
Medical Visual Question Answering: A Survey
Medical Visual Question Answering~(VQA) is a combination of medical artificial intelligence and popular VQA challenges.
Multimodal Transformer with Variable-length Memory for Vision-and-Language Navigation
Vision-and-Language Navigation (VLN) is a task that an agent is required to follow a language instruction to navigate to the goal position, which relies on the ongoing interactions with the environment during moving.
Multilingual Neural Machine Translation:Can Linguistic Hierarchies Help?
Multilingual Neural Machine Translation (MNMT) trains a single NMT model that supports translation between multiple languages, rather than training separate models for different languages.
It is Not as Good as You Think! Evaluating Simultaneous Machine Translation on Interpretation Data
Most existing simultaneous machine translation (SiMT) systems are trained and evaluated on offline translation corpora.
Pretrained Language Model in Continual Learning: A Comparative Study
In this paper, we thoroughly compare the continual learning performance over the combination of 5 PLMs and 4 veins of CL methods on 3 benchmarks in 2 typical incremental settings.
Generate, Annotate, and Learn: Generative Models Advance Self-Training and Knowledge Distillation
To obtain strong task-specific generative models, we either fine-tune a large language model (LLM) on inputs from specific tasks, or prompt a LLM with a few input examples to generate more unlabeled examples.
Total Recall: a Customized Continual Learning Method for Neural Semantic Parsers
We conduct extensive experiments to study the research problems involved in continual semantic parsing and demonstrate that a neural semantic parser trained with TotalRecall achieves superior performance than the one trained directly with the SOTA continual learning algorithms and achieve a 3-6 times speedup compared to re-training from scratch.
Generalised Unsupervised Domain Adaptation of Neural Machine Translation with Cross-Lingual Data Selection
Once the in-domain data is detected by the classifier, the NMT model is then adapted to the new domain by jointly learning translation and domain discrimination tasks.
Uncertainty-Aware Balancing for Multilingual and Multi-Domain Neural Machine Translation Training
In this work, we propose an approach, MultiUAT, that dynamically adjusts the training data usage based on the model's uncertainty on a small set of trusted clean data for multi-corpus machine translation.
Improving Numerical Reasoning Skills in the Modular Approach for Complex Question Answering on Text
Numerical reasoning skills are essential for complex question answering (CQA) over text.
Student Surpasses Teacher: Imitation Attack for Black-Box NLP APIs
Machine-learning-as-a-service (MLaaS) has attracted millions of users to their splendid large-scale models.
Learning to Explain: Generating Stable Explanations Fast
The importance of explaining the outcome of a machine learning model, especially a black-box model, is widely acknowledged.
Generate, Annotate, and Learn: NLP with Synthetic Text
This paper studies the use of language models as a source of synthetic unlabeled text for NLP.
Adaptive Knowledge-Enhanced Bayesian Meta-Learning for Few-shot Event Detection
Event detection (ED) aims at detecting event trigger words in sentences and classifying them into specific event types.
Neural-Symbolic Commonsense Reasoner with Relation Predictors
The dynamic nature of commonsense knowledge postulates models capable of performing multi-hop reasoning over new situations.
All Labels Are Not Created Equal: Enhancing Semi-supervision via Label Grouping and Co-training
We train two classifiers with two different views of the class labels: one classifier uses the one-hot view of the labels and disregards any potential similarity among the classes, while the other uses a distributed view of the labels and groups potentially similar classes together.
Few-Shot Semantic Parsing for New Predicates
In this work, we investigate the problems of semantic parsing in a few-shot learning setting.
Domain Adaptative Causality Encoder
Our experiments on four different benchmark causality datasets demonstrate the superiority of our approach over the existing baselines, by up to 7% improvement, on the tasks of identification and localisation of the causal relations from the text.
Multi-objective semi-supervised clustering to identify health service patterns for injured patients
The practical purpose of developing this pattern recognition method is to group patients, who are injured in transport accidents, in the early stages post-injury.
COSMO: Conditional SEQ2SEQ-based Mixture Model for Zero-Shot Commonsense Question Answering
However, current approaches in this realm lack the ability to perform commonsense reasoning upon facing an unseen situation, mostly due to incapability of identifying a diverse range of implicit social relations.
Context Dependent Semantic Parsing: A Survey
Semantic parsing is the task of translating natural language utterances into machine-readable meaning representations.
Retrieve, Program, Repeat: Complex Knowledge Base Question Answering via Alternate Meta-learning
However, this comes at the cost of manually labeling similar questions to learn a retrieval model, which is tedious and expensive.
Few-Shot Complex Knowledge Base Question Answering via Meta Reinforcement Learning
Our method achieves state-of-the-art performance on the CQA dataset (Saha et al., 2018) while using only five trial trajectories for the top-5 retrieved questions in each support set, and metatraining on tasks constructed from only 1% of the training set.
Knowledge Base Question Answering
Meta Reinforcement Learning
+3
Understanding Unnatural Questions Improves Reasoning over Text
A prominent approach to this task is based on the programmer-interpreter framework, where the programmer maps the question into a sequence of reasoning actions which is then executed on the raw text by the interpreter.
Collective Wisdom: Improving Low-resource Neural Machine Translation using Adaptive Knowledge Distillation
Scarcity of parallel sentence-pairs poses a significant hurdle for training high-quality Neural Machine Translation (NMT) models in bilingually low-resource scenarios.
Knowledge Distillation
Low-Resource Neural Machine Translation
+3
Leveraging Discourse Rewards for Document-Level Neural Machine Translation
Document-level machine translation focuses on the translation of entire documents from a source to a target language.
Scene Graph Modification Based on Natural Language Commands
In this paper, we explore the novel problem of graph modification, where the systems need to learn how to update an existing scene graph given a new user's command.
Effective Unsupervised Domain Adaptation with Adversarially Trained Language Models
Recent work has shown the importance of adaptation of broad-coverage contextualised embedding models on the domain of the target task of interest.
SummPip: Unsupervised Multi-Document Summarization with Sentence Graph Compression
Obtaining training data for multi-document summarization (MDS) is time consuming and resource-intensive, so recent neural models can only be trained for limited domains.
Dynamic Programming Encoding for Subword Segmentation in Neural Machine Translation
This paper introduces Dynamic Programming Encoding (DPE), a new segmentation algorithm for tokenizing sentences into subword units.
Decoding As Dynamic Programming For Recurrent Autoregressive Models
Decoding in autoregressive models (ARMs) consists of searching for a high scoring output sequence under the trained model.
Contextual Neural Machine Translation Improves Translation of Cataphoric Pronouns
In this work, we investigate the effect of future sentences as context by comparing the performance of a contextual NMT model trained with the future context to the one trained with the past context.
Learning Coupled Policies for Simultaneous Machine Translation using Imitation Learning
We present a novel approach to efficiently learn a simultaneous translation model with coupled programmer-interpreter policies.
Learning to Multi-Task Learn for Better Neural Machine Translation
We effectively and efficiently learn the training schedule policy within the imitation learning framework using an oracle policy algorithm that dynamically sets the importance weights of auxiliary tasks based on their contributions to the generalisability of the main NMT task.
A Survey on Document-level Neural Machine Translation: Methods and Evaluation
Machine translation (MT) is an important task in natural language processing (NLP) as it automates the translation process and reduces the reliance on human translators.
Question Generation from Paragraphs: A Tale of Two Hierarchical Models
Specifically, we propose (a) a novel hierarchical BiLSTM model with selective attention and (b) a novel hierarchical Transformer architecture, both of which learn hierarchical representations of paragraphs.
Adaptively Scheduled Multitask Learning: The Case of Low-Resource Neural Machine Translation
The role of training schedule becomes even more crucial in \textit{biased-MTL} where the goal is to improve one (or a subset) of tasks the most, e. g. translation quality.
Monash University's Submissions to the WNGT 2019 Document Translation Task
We describe the work of Monash University for the shared task of Rotowire document translation organised by the 3rd Workshop on Neural Generation and Translation (WNGT 2019).
Neural Speech Translation using Lattice Transformations and Graph Networks
Speech translation systems usually follow a pipeline approach, using word lattices as an intermediate representation.
Learning How to Active Learn by Dreaming
Heuristic-based active learning (AL) methods are limited when the data distribution of the underlying learning problems vary.
Selective Attention for Context-aware Neural Machine Translation
Despite the progress made in sentence-level NMT, current systems still fall short at achieving fluent, good quality translation for a full document.
Medical Multimodal Classifiers Under Scarce Data Condition
To overcome the challenges of the small training dataset which only has 3K frontal X-ray images and medical reports in pairs, we have adopted transfer learning for the multimodal which concatenates the layers of image and text submodels.
A new simple and effective measure for bag-of-word inter-document similarity measurement
To measure the similarity of two documents in the bag-of-words (BoW) vector representation, different term weighting schemes are used to improve the performance of cosine similarity---the most widely used inter-document similarity measure in text mining.
Improved Neural Machine Translation using Side Information
In this work, we investigate whether side information is helpful in neural machine translation (NMT).
Exploring Textual and Speech information in Dialogue Act Classification with Speaker Domain Adaptation
In spite of the recent success of Dialogue Act (DA) classification, the majority of prior works focus on text-based classification with oracle transcriptions, i. e. human transcriptions, instead of Automatic Speech Recognition (ASR)'s transcriptions.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+5
Sequence to Sequence Mixture Model for Diverse Machine Translation
In this paper, we develop a novel sequence to sequence mixture (S2SMIX) model that improves both translation diversity and quality by adopting a committee of specialized translation models rather than a single translation model.
Automatic Post-Editing of Machine Translation: A Neural Programmer-Interpreter Approach
Automated Post-Editing (PE) is the task of automatically correct common and repetitive errors found in machine translation (MT) output.
Learning to Actively Learn Neural Machine Translation
Traditional active learning (AL) methods for machine translation (MT) rely on heuristics.
Contextual Neural Model for Translating Bilingual Multi-Speaker Conversations
In this work, we propose the task of translating Bilingual Multi-Speaker Conversations, and explore neural architectures which exploit both source and target-side conversation histories for this task.
Incorporating Syntactic Uncertainty in Neural Machine Translation with a Forest-to-Sequence Model
Incorporating syntactic information in Neural Machine Translation (NMT) can lead to better reorderings, particularly useful when the language pairs are syntactically highly divergent or when the training bitext is not large.
Learning How to Actively Learn: A Deep Imitation Learning Approach
Heuristic-based active learning (AL) methods are limited when the data distribution of the underlying learning problems vary.
Iterative Back-Translation for Neural Machine Translation
We present iterative back-translation, a method for generating increasingly better synthetic parallel data from monolingual data to train neural machine translation systems.
Adaptive Knowledge Sharing in Multi-Task Learning: Improving Low-Resource Neural Machine Translation
The routing network enables adaptive collaboration by dynamic sharing of blocks conditioned on the task at hand, input, and model state.
Graph-to-Sequence Learning using Gated Graph Neural Networks
Many NLP applications can be framed as a graph-to-sequence learning problem.
The Context-Dependent Additive Recurrent Neural Net
Contextual sequence mapping is one of the fundamental problems in Natural Language Processing (NLP).
Neural Machine Translation for Bilingually Scarce Scenarios: A Deep Multi-task Learning Approach
Neural machine translation requires large amounts of parallel training text to learn a reasonable-quality translation model.
Incorporating Syntactic Uncertainty in Neural Machine Translation with Forest-to-Sequence Model
In this paper, we propose a forest-to-sequence Attentional Neural Machine Translation model to make use of exponentially many parse trees of the source sentence to compensate for the parser errors.
Document Context Neural Machine Translation with Memory Networks
We present a document-level neural machine translation model which takes both source and target document context into account using memory networks.
Preserving Distributional Information in Dialogue Act Classification
This paper introduces a novel training/decoding strategy for sequence labeling.
Towards Decoding as Continuous Optimisation in Neural Machine Translation
We propose a novel decoding approach for neural machine translation (NMT) based on continuous optimisation.
Persian-Spanish Low-Resource Statistical Machine Translation Through English as Pivot Language
This paper is an attempt to exclusively focus on investigating the pivot language technique in which a bridging language is utilized to increase the quality of the Persian-Spanish low-resource Statistical Machine Translation (SMT).
A Generative Attentional Neural Network Model for Dialogue Act Classification
We propose a novel generative neural network architecture for Dialogue Act classification.
A Hierarchical Neural Model for Learning Sequences of Dialogue Acts
We propose a novel hierarchical Recurrent Neural Network (RNN) for learning sequences of Dialogue Acts (DAs).
Efficient Benchmarking of NLP APIs using Multi-armed Bandits
Comparing NLP systems to select the best one for a task of interest, such as named entity recognition, is critical for practitioners and researchers.
Towards Decoding as Continuous Optimization in Neural Machine Translation
We propose a novel decoding approach for neural machine translation (NMT) based on continuous optimisation.
Improving Word Alignment of Rare Words with Word Embeddings
We address the problem of inducing word alignment for language pairs by developing an unsupervised model with the capability of getting applied to other generative alignment models.
Fast, Small and Exact: Infinite-order Language Modelling with Compressed Suffix Trees
Efficient methods for storing and querying are critical for scaling high-order n-gram language models to large corpora.
Word Representation Models for Morphologically Rich Languages in Neural Machine Translation
Dealing with the complex word forms in morphologically rich languages is an open problem in language processing, and is particularly important in translation.
Prepositional Attachment Disambiguation Using Bilingual Parsing and Alignments
In this paper, we attempt to solve the problem of Prepositional Phrase (PP) attachments in English.
A Latent Variable Recurrent Neural Network for Discourse Relation Language Models
This paper presents a novel latent variable recurrent neural network architecture for jointly modeling sequences of words and (possibly latent) discourse relations between adjacent sentences.
Incorporating Structural Alignment Biases into an Attentional Neural Translation Model
Neural encoder-decoder models of machine translation have achieved impressive results, rivalling traditional translation models.
Novel Bernstein-like Concentration Inequalities for the Missing Mass
We are concerned with obtaining novel concentration inequalities for the missing mass, i. e. the total probability mass of the outcomes not observed in the sample.
Structured Prediction of Sequences and Trees using Infinite Contexts
Linguistic structures exhibit a rich array of global phenomena, however commonly used Markov models are unable to adequately describe these phenomena due to their strong locality assumptions.
A Rate Distortion Approach for Semi-Supervised Conditional Random Fields
We propose a novel information theoretic approach for semi-supervised learning of conditional random fields.
