Search Results for author: Wael Hamza

Found 21 papers, 5 papers with code

Limitations of Knowledge Distillation for Zero-shot Transfer Learning

no code implementations EMNLP (sustainlp) 2021 Saleh Soltan, Haidar Khan, Wael Hamza

We demonstrate that in contradiction to the previous observation in the case of monolingual distillation, in multilingual settings, distillation during pretraining is more effective than distillation during fine-tuning for zero-shot transfer learning.

Knowledge Distillation Transfer Learning +1

Training Naturalized Semantic Parsers with Very Little Data

1 code implementation29 Apr 2022 Subendhu Rongali, Konstantine Arkoudas, Melanie Rubino, Wael Hamza

Semantic parsing is an important NLP problem, particularly for voice assistants such as Alexa and Google Assistant.

Semantic Parsing

Instilling Type Knowledge in Language Models via Multi-Task QA

1 code implementation28 Apr 2022 Shuyang Li, Mukund Sridhar, Chandana Satya Prakash, Jin Cao, Wael Hamza, Julian McAuley

Understanding human language often necessitates understanding entities and their place in a taxonomy of knowledge -- their types.

Knowledge Graphs

Contextual Domain Classification with Temporal Representations

no code implementations NAACL 2021 Tzu-Hsiang Lin, Yipeng Shi, Chentao Ye, Yang Fan, Weitong Ruan, Emre Barut, Wael Hamza, Chengwei Su

In commercial dialogue systems, the Spoken Language Understanding (SLU) component tends to have numerous domains thus context is needed to help resolve ambiguities.

Classification Spoken Language Understanding

Exploring Transfer Learning For End-to-End Spoken Language Understanding

no code implementations15 Dec 2020 Subendhu Rongali, Beiye Liu, Liwei Cai, Konstantine Arkoudas, Chengwei Su, Wael Hamza

Since our model can process both speech and text input sequences and learn to predict a target sequence, it also allows us to do zero-shot E2E SLU by training on only text-hypothesis data (without any speech) from a new domain.

Automatic Speech Recognition Natural Language Understanding +2

Multi-task Learning of Spoken Language Understanding by Integrating N-Best Hypotheses with Hierarchical Attention

no code implementations COLING 2020 Mingda Li, Xinyue Liu, Weitong Ruan, Luca Soldaini, Wael Hamza, Chengwei Su

The comparison shows that our model could recover the transcription by integrating the fragmented information among hypotheses and identifying the frequent error patterns of the ASR module, and even rewrite the query for a better understanding, which reveals the characteristic of multi-task learning of broadcasting knowledge.

Automatic Speech Recognition Intent Classification +3

Style Attuned Pre-training and Parameter Efficient Fine-tuning for Spoken Language Understanding

no code implementations9 Oct 2020 Jin Cao, Jun Wang, Wael Hamza, Kelly Vanee, Shang-Wen Li

The light encoder architecture separates the shared pre-trained networks from the mappings of generally encoded knowledge to specific domains of SLU, allowing for the domain adaptation to be performed solely at the light encoder and thus increasing efficiency.

Domain Adaptation Language Modelling +1

Don't Parse, Generate! A Sequence to Sequence Architecture for Task-Oriented Semantic Parsing

no code implementations30 Jan 2020 Subendhu Rongali, Luca Soldaini, Emilio Monti, Wael Hamza

Virtual assistants such as Amazon Alexa, Apple Siri, and Google Assistant often rely on a semantic parsing component to understand which action(s) to execute for an utterance spoken by its users.

Semantic Parsing Slot Filling

Improving Spoken Language Understanding By Exploiting ASR N-best Hypotheses

no code implementations11 Jan 2020 Mingda Li, Weitong Ruan, Xinyue Liu, Luca Soldaini, Wael Hamza, Chengwei Su

The NLU module usually uses the first best interpretation of a given speech in downstream tasks such as domain and intent classification.

Automatic Speech Recognition Intent Classification +2

Neural Cross-Lingual Coreference Resolution and its Application to Entity Linking

no code implementations ACL 2018 Gourab Kundu, Avirup Sil, Radu Florian, Wael Hamza

We propose an entity-centric neural cross-lingual coreference model that builds on multi-lingual embeddings and language-independent features.

Coreference Resolution Entity Linking

Leveraging Context Information for Natural Question Generation

1 code implementation NAACL 2018 Linfeng Song, Zhiguo Wang, Wael Hamza, Yue Zhang, Daniel Gildea

The task of natural question generation is to generate a corresponding question given the input passage (fact) and answer.

Question Generation

Neural Cross-Lingual Entity Linking

no code implementations5 Dec 2017 Avirup Sil, Gourab Kundu, Radu Florian, Wael Hamza

A major challenge in Entity Linking (EL) is making effective use of contextual information to disambiguate mentions to Wikipedia that might refer to different entities in different contexts.

Cross-Lingual Entity Linking Entity Disambiguation +3

A Unified Query-based Generative Model for Question Generation and Question Answering

no code implementations4 Sep 2017 Linfeng Song, Zhiguo Wang, Wael Hamza

In the QG task, a question is generated from the system given the passage and the target answer, whereas in the QA task, the answer is generated given the question and the passage.

Question Answering Question Generation

$k$-Nearest Neighbor Augmented Neural Networks for Text Classification

no code implementations25 Aug 2017 Zhiguo Wang, Wael Hamza, Linfeng Song

However, it lacks the capacity of utilizing instance-level information from individual instances in the training set.

Classification General Classification +2

Reinforcement Learning for Transition-Based Mention Detection

no code implementations13 Mar 2017 Georgiana Dinu, Wael Hamza, Radu Florian

This paper describes an application of reinforcement learning to the mention detection task.

reinforcement-learning

Multi-Perspective Context Matching for Machine Comprehension

1 code implementation13 Dec 2016 Zhiguo Wang, Haitao Mi, Wael Hamza, Radu Florian

Based on this dataset, we propose a Multi-Perspective Context Matching (MPCM) model, which is an end-to-end system that directly predicts the answer beginning and ending points in a passage.

Question Answering Reading Comprehension

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