Search Results for author: Gholamreza Haffari

Found 99 papers, 30 papers with code

Personal Information Leakage Detection in Conversations

1 code implementation EMNLP 2020 Qiongkai Xu, Lizhen Qu, Zeyu Gao, Gholamreza Haffari

In this work, we propose to protect personal information by warning users of detected suspicious sentences generated by conversational assistants.

Language Modelling

Lifelong Explainer for Lifelong Learners

1 code implementation EMNLP 2021 Xuelin Situ, Sameen Maruf, Ingrid Zukerman, Cecile Paris, Gholamreza Haffari

Our ablation study shows that the ER mechanism in our LLE approach enhances the learning capabilities of the student explainer.

Text Classification

Multilingual Neural Machine Translation: Can Linguistic Hierarchies Help?

no code implementations Findings (EMNLP) 2021 Fahimeh Saleh, Wray Buntine, Gholamreza Haffari, Lan Du

Multilingual Neural Machine Translation (MNMT) trains a single NMT model that supports translation between multiple languages, rather than training separate models for different languages.

Knowledge Distillation Machine Translation +1

Explaining Decision-Tree Predictions by Addressing Potential Conflicts between Predictions and Plausible Expectations

no code implementations INLG (ACL) 2021 Sameen Maruf, Ingrid Zukerman, Ehud Reiter, Gholamreza Haffari

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.

Utilizing Wordnets for Cognate Detection among Indian Languages

no code implementations GWC 2019 Diptesh Kanojia, Kevin Patel, Pushpak Bhattacharyya, Malhar Kulkarni, Gholamreza Haffari

Automatic Cognate Detection (ACD) is a challenging task which has been utilized to help NLP applications like Machine Translation, Information Retrieval and Computational Phylogenetics.

Information Retrieval Machine Translation

Challenge Dataset of Cognates and False Friend Pairs from Indian Languages

1 code implementation LREC 2020 Diptesh Kanojia, Pushpak Bhattacharyya, Malhar Kulkarni, Gholamreza Haffari

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.

Information Retrieval Machine Translation +1

Cognition-aware Cognate Detection

1 code implementation EACL 2021 Diptesh Kanojia, Prashant Sharma, Sayali Ghodekar, Pushpak Bhattacharyya, Gholamreza Haffari, Malhar Kulkarni

We collect gaze behaviour data for a small sample of cognates and show that extracted cognitive features help the task of cognate detection.

Information Retrieval Machine Translation +4

BaLeNAS: Differentiable Architecture Search via the Bayesian Learning Rule

no code implementations25 Nov 2021 Miao Zhang, Jilin Hu, Steven Su, Shirui Pan, Xiaojun Chang, Bin Yang, Gholamreza Haffari

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.

Neural Architecture Search Variational Inference

Medical Visual Question Answering: A Survey

no code implementations19 Nov 2021 Zhihong Lin, Donghao Zhang, Qingyi Tac, Danli Shi, Gholamreza Haffari, Qi Wu, Mingguang He, ZongYuan Ge

Medical Visual Question Answering~(VQA) is a combination of medical artificial intelligence and popular VQA challenges.

Medical Visual Question Answering Question Answering +1

Multimodal Transformer with Variable-length Memory for Vision-and-Language Navigation

no code implementations10 Nov 2021 Chuang Lin, Yi Jiang, Jianfei Cai, Lizhen Qu, Gholamreza Haffari, Zehuan Yuan

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.

Vision and Language Navigation

Multilingual Neural Machine Translation:Can Linguistic Hierarchies Help?

no code implementations15 Oct 2021 Fahimeh Saleh, Wray Buntine, Gholamreza Haffari, Lan Du

Multilingual Neural Machine Translation (MNMT) trains a single NMT model that supports translation between multiple languages, rather than training separate models for different languages.

Knowledge Distillation Machine Translation +1

Total Recall: a Customized Continual Learning Method for Neural Semantic Parsers

1 code implementation EMNLP 2021 Zhuang Li, Lizhen Qu, Gholamreza Haffari

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.

Continual Learning Semantic Parsing

Generalised Unsupervised Domain Adaptation of Neural Machine Translation with Cross-Lingual Data Selection

no code implementations EMNLP 2021 Thuy-Trang Vu, Xuanli He, Dinh Phung, Gholamreza Haffari

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.

Contrastive Learning Machine Translation +2

Uncertainty-Aware Balancing for Multilingual and Multi-Domain Neural Machine Translation Training

no code implementations EMNLP 2021 Minghao Wu, Yitong Li, Meng Zhang, Liangyou Li, Gholamreza Haffari, Qun Liu

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.

Machine Translation Translation

Beyond Model Extraction: Imitation Attack for Black-Box NLP APIs

no code implementations29 Aug 2021 Qiongkai Xu, Xuanli He, Lingjuan Lyu, Lizhen Qu, Gholamreza Haffari

Machine-learning-as-a-service (MLaaS) has attracted millions of users to their outperforming sophisticated models.

Model extraction Unsupervised Domain Adaptation

Learning to Explain: Generating Stable Explanations Fast

1 code implementation ACL 2021 Xuelin Situ, Ingrid Zukerman, Cecile Paris, Sameen Maruf, Gholamreza Haffari

The importance of explaining the outcome of a machine learning model, especially a black-box model, is widely acknowledged.

Differentiable Architecture Search Meets Network Pruning at Initialization: A More Reliable, Efficient, and Flexible Framework

no code implementations22 Jun 2021 Miao Zhang, Steven Su, Shirui Pan, Xiaojun Chang, Wei Huang, Bin Yang, Gholamreza Haffari

Although Differentiable ARchiTecture Search (DARTS) has become the mainstream paradigm in Neural Architecture Search (NAS) due to its simplicity and efficiency, more recent works found that the performance of the searched architecture barely increases with the optimization proceeding in DARTS, and the final magnitudes obtained by DARTS could hardly indicate the importance of operations.

Network Pruning Neural Architecture Search

Generate, Annotate, and Learn: NLP with Synthetic Text

no code implementations11 Jun 2021 Xuanli He, Islam Nassar, Jamie Kiros, Gholamreza Haffari, Mohammad Norouzi

To obtain a strong task-specific LM, we either fine-tune a large LM on inputs from a specific task, or prompt a large LM with a few input examples and conditionally generate more unlabeled examples.

Few-Shot Learning Knowledge Distillation +1

All Labels Are Not Created Equal: Enhancing Semi-supervision via Label Grouping and Co-training

1 code implementation CVPR 2021 Islam Nassar, Samitha Herath, Ehsan Abbasnejad, Wray Buntine, Gholamreza Haffari

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.

Semi-Supervised Image Classification

Domain Adaptative Causality Encoder

1 code implementation ALTA 2020 Farhad Moghimifar, Gholamreza Haffari, Mahsa Baktashmotlagh

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

no code implementations16 Nov 2020 Hadi Akbarzadeh Khorshidi, Uwe Aickelin, Gholamreza Haffari, Behrooz Hassani-Mahmooei

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

1 code implementation COLING 2020 Farhad Moghimifar, Lizhen Qu, Yue Zhuo, Mahsa Baktashmotlagh, Gholamreza Haffari

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.

Question Answering

Context Dependent Semantic Parsing: A Survey

1 code implementation COLING 2020 Zhuang Li, Lizhen Qu, Gholamreza Haffari

Semantic parsing is the task of translating natural language utterances into machine-readable meaning representations.

Semantic Parsing

Retrieve, Program, Repeat: Complex Knowledge Base Question Answering via Alternate Meta-learning

1 code implementation29 Oct 2020 Yuncheng Hua, Yuan-Fang Li, Gholamreza Haffari, Guilin Qi, Wei Wu

However, this comes at the cost of manually labeling similar questions to learn a retrieval model, which is tedious and expensive.

Knowledge Base Question Answering Meta-Learning

Few-Shot Complex Knowledge Base Question Answering via Meta Reinforcement Learning

1 code implementation EMNLP 2020 Yuncheng Hua, Yuan-Fang Li, Gholamreza Haffari, Guilin Qi, Tongtong Wu

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 +1

Understanding Unnatural Questions Improves Reasoning over Text

no code implementations COLING 2020 Xiao-Yu Guo, Yuan-Fang Li, Gholamreza Haffari

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.

Question Answering

Effective Unsupervised Domain Adaptation with Adversarially Trained Language Models

1 code implementation EMNLP 2020 Thuy-Trang Vu, Dinh Phung, Gholamreza Haffari

Recent work has shown the importance of adaptation of broad-coverage contextualised embedding models on the domain of the target task of interest.

Named Entity Recognition Unsupervised Domain Adaptation

SummPip: Unsupervised Multi-Document Summarization with Sentence Graph Compression

1 code implementation17 Jul 2020 Jinming Zhao, Ming Liu, Longxiang Gao, Yuan Jin, Lan Du, He Zhao, He Zhang, Gholamreza Haffari

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.

Document Summarization Multi-Document Summarization

Dynamic Programming Encoding for Subword Segmentation in Neural Machine Translation

1 code implementation ACL 2020 Xuanli He, Gholamreza Haffari, Mohammad Norouzi

This paper introduces Dynamic Programming Encoding (DPE), a new segmentation algorithm for tokenizing sentences into subword units.

Machine Translation Translation

Decoding As Dynamic Programming For Recurrent Autoregressive Models

no code implementations ICLR 2020 Najam Zaidi, Trevor Cohn, Gholamreza Haffari

Decoding in autoregressive models (ARMs) consists of searching for a high scoring output sequence under the trained model.

Text Infilling

Contextual Neural Machine Translation Improves Translation of Cataphoric Pronouns

1 code implementation ACL 2020 KayYen Wong, Sameen Maruf, Gholamreza Haffari

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.

Machine Translation Translation

Learning to Multi-Task Learn for Better Neural Machine Translation

no code implementations10 Jan 2020 Poorya Zaremoodi, Gholamreza Haffari

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.

Imitation Learning Machine Translation +2

A Survey on Document-level Neural Machine Translation: Methods and Evaluation

1 code implementation18 Dec 2019 Sameen Maruf, Fahimeh Saleh, Gholamreza Haffari

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.

Document Level Machine Translation Machine Translation +1

Question Generation from Paragraphs: A Tale of Two Hierarchical Models

no code implementations8 Nov 2019 Vishwajeet Kumar, Raktim Chaki, Sai Teja Talluri, Ganesh Ramakrishnan, Yuan-Fang Li, Gholamreza Haffari

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.

Question Generation

Adaptively Scheduled Multitask Learning: The Case of Low-Resource Neural Machine Translation

no code implementations WS 2019 Poorya Zaremoodi, Gholamreza Haffari

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.

Low-Resource Neural Machine Translation Translation

Neural Speech Translation using Lattice Transformations and Graph Networks

no code implementations WS 2019 Daniel Beck, Trevor Cohn, Gholamreza Haffari

Speech translation systems usually follow a pipeline approach, using word lattices as an intermediate representation.


Monash University's Submissions to the WNGT 2019 Document Translation Task

no code implementations WS 2019 Sameen Maruf, Gholamreza Haffari

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).

Document Translation Machine Translation +1

Learning How to Active Learn by Dreaming

1 code implementation ACL 2019 Thuy-Trang Vu, Ming Liu, Dinh Phung, Gholamreza Haffari

Heuristic-based active learning (AL) methods are limited when the data distribution of the underlying learning problems vary.

Active Learning Named Entity Recognition +1

Selective Attention for Context-aware Neural Machine Translation

1 code implementation NAACL 2019 Sameen Maruf, André F. T. Martins, Gholamreza Haffari

Despite the progress made in sentence-level NMT, current systems still fall short at achieving fluent, good quality translation for a full document.

Machine Translation Translation

Medical Multimodal Classifiers Under Scarce Data Condition

no code implementations24 Feb 2019 Faik Aydin, Maggie Zhang, Michelle Ananda-Rajah, Gholamreza Haffari

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.

Transfer Learning

A new simple and effective measure for bag-of-word inter-document similarity measurement

no code implementations9 Feb 2019 Sunil Aryal, Kai Ming Ting, Takashi Washio, Gholamreza Haffari

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.

Exploring Textual and Speech information in Dialogue Act Classification with Speaker Domain Adaptation

no code implementations ALTA 2018 Xuanli He, Quan Hung Tran, William Havard, Laurent Besacier, Ingrid Zukerman, Gholamreza Haffari

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.

Dialogue Act Classification Domain Adaptation +2

Sequence to Sequence Mixture Model for Diverse Machine Translation

no code implementations CONLL 2018 Xuanli He, Gholamreza Haffari, Mohammad Norouzi

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.

Machine Translation Translation

Automatic Post-Editing of Machine Translation: A Neural Programmer-Interpreter Approach

1 code implementation EMNLP 2018 Thuy-Trang Vu, Gholamreza Haffari

Automated Post-Editing (PE) is the task of automatically correct common and repetitive errors found in machine translation (MT) output.

Automatic Post-Editing Translation

Contextual Neural Model for Translating Bilingual Multi-Speaker Conversations

1 code implementation WS 2018 Sameen Maruf, André F. T. Martins, Gholamreza Haffari

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.

Document Translation Machine Translation +1

Incorporating Syntactic Uncertainty in Neural Machine Translation with a Forest-to-Sequence Model

no code implementations COLING 2018 Poorya Zaremoodi, Gholamreza Haffari

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.

Machine Translation Translation

Learning How to Actively Learn: A Deep Imitation Learning Approach

1 code implementation ACL 2018 Ming Liu, Wray Buntine, Gholamreza Haffari

Heuristic-based active learning (AL) methods are limited when the data distribution of the underlying learning problems vary.

Active Learning General Classification +3

Iterative Back-Translation for Neural Machine Translation

no code implementations WS 2018 Vu Cong Duy Hoang, Philipp Koehn, Gholamreza Haffari, Trevor Cohn

We present iterative back-translation, a method for generating increasingly better synthetic parallel data from monolingual data to train neural machine translation systems.

Machine Translation Translation

Incorporating Syntactic Uncertainty in Neural Machine Translation with Forest-to-Sequence Model

no code implementations19 Nov 2017 Poorya Zaremoodi, Gholamreza Haffari

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.

Machine Translation Translation

Document Context Neural Machine Translation with Memory Networks

no code implementations ACL 2018 Sameen Maruf, Gholamreza Haffari

We present a document-level neural machine translation model which takes both source and target document context into account using memory networks.

Machine Translation Structured Prediction +1

Persian-Spanish Low-Resource Statistical Machine Translation Through English as Pivot Language

no code implementations RANLP 2017 Benyamin Ahmadnia, Javier Serrano, Gholamreza Haffari

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).

Machine Translation Translation

Efficient Benchmarking of NLP APIs using Multi-armed Bandits

no code implementations EACL 2017 Gholamreza Haffari, Tuan Dung Tran, Mark Carman

Comparing NLP systems to select the best one for a task of interest, such as named entity recognition, is critical for practitioners and researchers.

Multi-Armed Bandits Named Entity Recognition +1

Towards Decoding as Continuous Optimization in Neural Machine Translation

no code implementations11 Jan 2017 Cong Duy Vu Hoang, Gholamreza Haffari, Trevor Cohn

We propose a novel decoding approach for neural machine translation (NMT) based on continuous optimisation.

Machine Translation Translation

Improving Word Alignment of Rare Words with Word Embeddings

no code implementations COLING 2016 Masoud Jalili Sabet, Heshaam Faili, Gholamreza Haffari

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.

Machine Translation Word Alignment +1

Word Representation Models for Morphologically Rich Languages in Neural Machine Translation

no code implementations WS 2017 Ekaterina Vylomova, Trevor Cohn, Xuanli He, Gholamreza Haffari

Dealing with the complex word forms in morphologically rich languages is an open problem in language processing, and is particularly important in translation.

Machine Translation Translation

A Latent Variable Recurrent Neural Network for Discourse Relation Language Models

1 code implementation7 Mar 2016 Yangfeng Ji, Gholamreza Haffari, Jacob Eisenstein

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.

Dialog Act Classification General Classification +2

Novel Bernstein-like Concentration Inequalities for the Missing Mass

no code implementations10 Mar 2015 Bahman Yari Saeed Khanloo, Gholamreza Haffari

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.

Learning Theory

Structured Prediction of Sequences and Trees using Infinite Contexts

no code implementations9 Mar 2015 Ehsan Shareghi, Gholamreza Haffari, Trevor Cohn, Ann Nicholson

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.

Part-Of-Speech Tagging Structured Prediction

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