no code implementations • CoNLL (EMNLP) 2021 • Katharina Weitz, Lindsey Vanderlyn, Ngoc Thang Vu, Elisabeth André
We conclude that creating shared mental models between users and AI systems is important to achieving successful dialogs.
no code implementations • LREC 2022 • Nadja Schauffler, Toni Bernhart, Andre Blessing, Gunilla Eschenbach, Markus Gärtner, Kerstin Jung, Anna Kinder, Julia Koch, Sandra Richter, Gabriel Viehhauser, Ngoc Thang Vu, Lorenz Wesemann, Jonas Kuhn
We present the steps taken towards an exploration platform for a multi-modal corpus of German lyric poetry from the Romantic era developed in the project »textklang«.
no code implementations • ACL (IWSLT) 2021 • Pavel Denisov, Manuel Mager, Ngoc Thang Vu
This paper describes the submission to the IWSLT 2021 Low-Resource Speech Translation Shared Task by IMS team.
Automatic Speech Recognition Automatic Speech Recognition (ASR) +5
no code implementations • EMNLP (sustainlp) 2020 • Cennet Oguz, Ngoc Thang Vu
In this paper, we propose a novel two-stage model architecture that can be trained with only a few in-domain hand-labeled examples.
no code implementations • CoNLL (EMNLP) 2021 • Lindsey Vanderlyn, Gianna Weber, Michael Neumann, Dirk Väth, Sarina Meyer, Ngoc Thang Vu
Based on statistical and qualitative analysis of the responses, we found language style played an important role in how human-like participants perceived a dialog agent as well as how likable.
1 code implementation • MSR (COLING) 2020 • Xiang Yu, Simon Tannert, Ngoc Thang Vu, Jonas Kuhn
We introduce the IMS contribution to the Surface Realization Shared Task 2020.
no code implementations • NAACL (AmericasNLP) 2021 • Manuel Mager, Arturo Oncevay, Abteen Ebrahimi, John Ortega, Annette Rios, Angela Fan, Ximena Gutierrez-Vasques, Luis Chiruzzo, Gustavo Giménez-Lugo, Ricardo Ramos, Ivan Vladimir Meza Ruiz, Rolando Coto-Solano, Alexis Palmer, Elisabeth Mager-Hois, Vishrav Chaudhary, Graham Neubig, Ngoc Thang Vu, Katharina Kann
This paper presents the results of the 2021 Shared Task on Open Machine Translation for Indigenous Languages of the Americas.
no code implementations • 15 May 2024 • Maximilian Schmidt, Andrea Bartezzaghi, Ngoc Thang Vu
With this motivation, we show that using large language models can improve Question Answering performance on various datasets in the few-shot setting compared to state-of-the-art approaches.
1 code implementation • 16 Apr 2024 • Pavel Denisov, Ngoc Thang Vu
Our zero-shot evaluation results confirm the robustness of our approach across multiple tasks, including speech translation and multilingual spoken language understanding, thereby opening new avenues for applying LLMs in the speech domain.
no code implementations • 26 Mar 2024 • Dirk Väth, Lindsey Vanderlyn, Ngoc Thang Vu
Conversational Tree Search (V\"ath et al., 2023) is a recent approach to controllable dialog systems, where domain experts shape the behavior of a Reinforcement Learning agent through a dialog tree.
1 code implementation • 26 Mar 2024 • Pascal Tilli, Ngoc Thang Vu
In this work, we introduce an interpretable approach for graph-based VQA and demonstrate competitive performance on the GQA dataset.
no code implementations • 8 Mar 2024 • Wei Zhou, Heike Adel, Hendrik Schuff, Ngoc Thang Vu
Attribution scores indicate the importance of different input parts and can, thus, explain model behaviour.
no code implementations • 26 Oct 2023 • Florian Lux, Pascal Tilli, Sarina Meyer, Ngoc Thang Vu
Customizing voice and speaking style in a speech synthesis system with intuitive and fine-grained controls is challenging, given that little data with appropriate labels is available.
1 code implementation • 26 Oct 2023 • Florian Lux, Julia Koch, Sarina Meyer, Thomas Bott, Nadja Schauffler, Pavel Denisov, Antje Schweitzer, Ngoc Thang Vu
For our contribution to the Blizzard Challenge 2023, we improved on the system we submitted to the Blizzard Challenge 2021.
no code implementations • 23 Oct 2023 • Injy Hamed, Nizar Habash, Ngoc Thang Vu
Linguistic theories and random lexical replacement prove to be effective in the lack of CSW parallel data, where both approaches achieve similar results.
1 code implementation • 9 Oct 2023 • Pavel Denisov, Ngoc Thang Vu
A number of methods have been proposed for End-to-End Spoken Language Understanding (E2E-SLU) using pretrained models, however their evaluation often lacks multilingual setup and tasks that require prediction of lexical fillers, such as slot filling.
no code implementations • 11 Jun 2023 • Manuel Mager, Rajat Bhatnagar, Graham Neubig, Ngoc Thang Vu, Katharina Kann
Neural models have drastically advanced state of the art for machine translation (MT) between high-resource languages.
no code implementations • 31 May 2023 • Manuel Mager, Elisabeth Mager, Katharina Kann, Ngoc Thang Vu
In recent years machine translation has become very successful for high-resource language pairs.
1 code implementation • 4 May 2023 • Alon Jacovi, Hendrik Schuff, Heike Adel, Ngoc Thang Vu, Yoav Goldberg
Word-level saliency explanations ("heat maps over words") are often used to communicate feature-attribution in text-based models.
no code implementations • 10 Apr 2023 • Daniel Ortega, Chia-Yu Li, Ngoc Thang Vu
This paper presents our latest investigation on modeling backchannel in conversations.
no code implementations • 10 Apr 2023 • Daniel Ortega, Sarina Meyer, Antje Schweitzer, Ngoc Thang Vu
We present our latest findings on backchannel modeling novelly motivated by the canonical use of the minimal responses Yeah and Uh-huh in English and their correspondent tokens in German, and the effect of encoding the speaker-listener interaction.
1 code implementation • 17 Mar 2023 • Dirk Väth, Lindsey Vanderlyn, Ngoc Thang Vu
Conversational interfaces provide a flexible and easy way for users to seek information that may otherwise be difficult or inconvenient to obtain.
no code implementations • 22 Nov 2022 • Injy Hamed, Nizar Habash, Slim Abdennadher, Ngoc Thang Vu
We present our work on collecting ArzEn-ST, a code-switched Egyptian Arabic - English Speech Translation Corpus.
no code implementations • 21 Oct 2022 • Florian Lux, Ching-Yi Chen, Ngoc Thang Vu
This pretrained model is then finetuned to a specific task.
1 code implementation • 21 Oct 2022 • Florian Lux, Julia Koch, Ngoc Thang Vu
While neural methods for text-to-speech (TTS) have shown great advances in modeling multiple speakers, even in zero-shot settings, the amount of data needed for those approaches is generally not feasible for the vast majority of the world's over 6, 000 spoken languages.
no code implementations • 20 Oct 2022 • Chia-Yu Li, Ngoc Thang Vu
In this paper, we exploit the advantages from both inter-domain loss and CycleGAN to achieve better shared representation of unpaired speech and text inputs and thus improve the speech-to-text mapping.
Automatic Speech Recognition Automatic Speech Recognition (ASR) +1
1 code implementation • 13 Oct 2022 • Sarina Meyer, Pascal Tilli, Pavel Denisov, Florian Lux, Julia Koch, Ngoc Thang Vu
In order to protect the privacy of speech data, speaker anonymization aims for hiding the identity of a speaker by changing the voice in speech recordings.
no code implementations • 13 Oct 2022 • Hendrik Schuff, Heike Adel, Peng Qi, Ngoc Thang Vu
This approach assumes that explanations which reach higher proxy scores will also provide a greater benefit to human users.
no code implementations • 11 Oct 2022 • Marwa Gaser, Manuel Mager, Injy Hamed, Nizar Habash, Slim Abdennadher, Ngoc Thang Vu
For extreme low-resource scenarios, a combination of frequency and morphology-based segmentations is shown to perform the best.
no code implementations • 31 Jul 2022 • Injy Hamed, Alia El Bolock, Cornelia Herbert, Slim Abdennadher, Ngoc Thang Vu
Given that the factors giving rise to CS vary from one country to the other, as well as from one person to the other, CS is found to be a speaker-dependant behaviour, where the frequency by which the foreign language is embedded differs across speakers.
no code implementations • 11 Jul 2022 • Julia Koch, Florian Lux, Nadja Schauffler, Toni Bernhart, Felix Dieterle, Jonas Kuhn, Sandra Richter, Gabriel Viehhauser, Ngoc Thang Vu
Speech synthesis for poetry is challenging due to specific intonation patterns inherent to poetic speech.
1 code implementation • 11 Jul 2022 • Sarina Meyer, Florian Lux, Pavel Denisov, Julia Koch, Pascal Tilli, Ngoc Thang Vu
In this work, we propose a speaker anonymization pipeline that leverages high quality automatic speech recognition and synthesis systems to generate speech conditioned on phonetic transcriptions and anonymized speaker embeddings.
Automatic Speech Recognition Automatic Speech Recognition (ASR) +2
2 code implementations • 24 Jun 2022 • Florian Lux, Julia Koch, Ngoc Thang Vu
The cloning of a speaker's voice using an untranscribed reference sample is one of the great advances of modern neural text-to-speech (TTS) methods.
no code implementations • 25 May 2022 • Injy Hamed, Nizar Habash, Slim Abdennadher, Ngoc Thang Vu
Results show that using a predictive model results in more natural CS sentences compared to the random approach, as reported in human judgements.
Automatic Speech Recognition Automatic Speech Recognition (ASR) +5
no code implementations • NAACL 2022 • Hung-Yi Lee, Shang-Wen Li, Ngoc Thang Vu
Deep learning has been the mainstream technique in natural language processing (NLP) area.
no code implementations • Findings (ACL) 2022 • Manuel Mager, Arturo Oncevay, Elisabeth Mager, Katharina Kann, Ngoc Thang Vu
Morphologically-rich polysynthetic languages present a challenge for NLP systems due to data sparsity, and a common strategy to handle this issue is to apply subword segmentation.
1 code implementation • ACL 2022 • Florian Lux, Ngoc Thang Vu
While neural text-to-speech systems perform remarkably well in high-resource scenarios, they cannot be applied to the majority of the over 6, 000 spoken languages in the world due to a lack of appropriate training data.
1 code implementation • 27 Jan 2022 • Hendrik Schuff, Alon Jacovi, Heike Adel, Yoav Goldberg, Ngoc Thang Vu
In this work, we focus on this question through a study of saliency-based explanations over textual data.
no code implementations • 19 Dec 2021 • Chia Yu Li, Ngoc Thang Vu
We investigate densely connected convolutional networks (DenseNets) and their extension with domain adversarial training for noise robust speech recognition.
no code implementations • 19 Dec 2021 • Chia-Yu Li, Ngoc Thang Vu
Code-Switching (CS) is a common linguistic phenomenon in multilingual communities that consists of switching between languages while speaking.
Automatic Speech Recognition Automatic Speech Recognition (ASR) +3
no code implementations • 13 Dec 2021 • Injy Hamed, Alia El Bolock, Nader Rizk, Cornelia Herbert, Slim Abdennadher, Ngoc Thang Vu
Multilingual speakers tend to alternate between languages within a conversation, a phenomenon referred to as "code-switching" (CS).
no code implementations • 12 Dec 2021 • Chia-Yu Li, Ngoc Thang Vu
This paper presents our latest investigations on improving automatic speech recognition for noisy speech via speech enhancement.
Automatic Speech Recognition Automatic Speech Recognition (ASR) +2
no code implementations • 12 Dec 2021 • Chia-Yu Li, Ngoc Thang Vu
This paper presents our latest effort on improving Code-switching language models that suffer from data scarcity.
Automatic Speech Recognition Automatic Speech Recognition (ASR) +2
2 code implementations • 29 Nov 2021 • Siddhant Arora, Siddharth Dalmia, Pavel Denisov, Xuankai Chang, Yushi Ueda, Yifan Peng, Yuekai Zhang, Sujay Kumar, Karthik Ganesan, Brian Yan, Ngoc Thang Vu, Alan W Black, Shinji Watanabe
However, there are few open source toolkits that can be used to generate reproducible results on different Spoken Language Understanding (SLU) benchmarks.
1 code implementation • EMNLP (ACL) 2021 • Dirk Väth, Pascal Tilli, Ngoc Thang Vu
On the way towards general Visual Question Answering (VQA) systems that are able to answer arbitrary questions, the need arises for evaluation beyond single-metric leaderboards for specific datasets.
1 code implementation • EMNLP (BlackboxNLP) 2021 • Hendrik Schuff, Hsiu-Yu Yang, Heike Adel, Ngoc Thang Vu
For this, we investigate different sources of external knowledge and evaluate the performance of our models on in-domain data as well as on special transfer datasets that are designed to assess fine-grained reasoning capabilities.
no code implementations • 29 Aug 2021 • Injy Hamed, Pavel Denisov, Chia-Yu Li, Mohamed Elmahdy, Slim Abdennadher, Ngoc Thang Vu
In this paper, we present our work on code-switched Egyptian Arabic-English automatic speech recognition (ASR).
Automatic Speech Recognition Automatic Speech Recognition (ASR) +2
no code implementations • ACL 2021 • Hung-Yi Lee, Ngoc Thang Vu, Shang-Wen Li
Meta-learning is one of the most important new techniques in machine learning in recent years.
no code implementations • 26 Jul 2021 • Hendrik Schuff, Heike Adel, Ngoc Thang Vu
In addition, we conduct a qualitative analysis of thought flow correction patterns and explore how thought flow predictions affect human users within a crowdsourcing study.
no code implementations • 30 Jun 2021 • Pavel Denisov, Manuel Mager, Ngoc Thang Vu
This paper describes the submission to the IWSLT 2021 Low-Resource Speech Translation Shared Task by IMS team.
Automatic Speech Recognition Automatic Speech Recognition (ASR) +5
1 code implementation • ACL 2022 • Abteen Ebrahimi, Manuel Mager, Arturo Oncevay, Vishrav Chaudhary, Luis Chiruzzo, Angela Fan, John Ortega, Ricardo Ramos, Annette Rios, Ivan Meza-Ruiz, Gustavo A. Giménez-Lugo, Elisabeth Mager, Graham Neubig, Alexis Palmer, Rolando Coto-Solano, Ngoc Thang Vu, Katharina Kann
Continued pretraining offers improvements, with an average accuracy of 44. 05%.
no code implementations • EACL 2021 • Cennet Oguz, Ngoc Thang Vu
Metric-based learning is a well-known family of methods for few-shot learning, especially in computer vision.
no code implementations • 2 Mar 2021 • Michael Neumann, Ngoc Thang Vu
In this paper we explore audiovisual emotion recognition under noisy acoustic conditions with a focus on speech features.
no code implementations • 25 Feb 2021 • Florian Lux, Ngoc Thang Vu
We propose a new method of generating meaningful embeddings for speech, changes to four commonly used meta learning approaches to enable them to perform keyword spotting in continuous signals and an approach of combining their outcomes into an end-to-end automatic speech recognition system to improve rare word recognition.
Automatic Speech Recognition Automatic Speech Recognition (ASR) +3
no code implementations • COLING 2020 • Daniel Grießhaber, Johannes Maucher, Ngoc Thang Vu
Recently, leveraging pre-trained Transformer based language models in down stream, task specific models has advanced state of the art results in natural language understanding tasks.
1 code implementation • EMNLP 2020 • Hendrik Schuff, Heike Adel, Ngoc Thang Vu
The user study shows that our models increase the ability of the users to judge the correctness of the system and that scores like F1 are not enough to estimate the usefulness of a model in a practical setting with human users.
no code implementations • CONLL 2020 • Ekta Sood, Simon Tannert, Diego Frassinelli, Andreas Bulling, Ngoc Thang Vu
We compare state of the art networks based on long short-term memory (LSTM), convolutional neural models (CNN) and XLNet Transformer architectures.
no code implementations • 3 Jul 2020 • Pavel Denisov, Ngoc Thang Vu
Spoken language understanding is typically based on pipeline architectures including speech recognition and natural language understanding steps.
no code implementations • WS 2020 • Xiang Yu, Ngoc Thang Vu, Jonas Kuhn
We present an iterative data augmentation framework, which trains and searches for an optimal ensemble and simultaneously annotates new training data in a self-training style.
no code implementations • ACL 2020 • Xiang Yu, Simon Tannert, Ngoc Thang Vu, Jonas Kuhn
We propose a graph-based method to tackle the dependency tree linearization task.
1 code implementation • ACL 2020 • Chia-Yu Li, Daniel Ortega, Dirk Väth, Florian Lux, Lindsey Vanderlyn, Maximilian Schmidt, Michael Neumann, Moritz Völkel, Pavel Denisov, Sabrina Jenne, Zorica Kacarevic, Ngoc Thang Vu
We present ADVISER - an open-source, multi-domain dialog system toolkit that enables the development of multi-modal (incorporating speech, text and vision), socially-engaged (e. g. emotion recognition, engagement level prediction and backchanneling) conversational agents.
no code implementations • LREC 2020 • Mohamed Balabel, Injy Hamed, Slim Abdennadher, Ngoc Thang Vu, {\"O}zlem {\c{C}}etino{\u{g}}lu
Code-switching has become a prevalent phenomenon across many communities.
no code implementations • LREC 2020 • Injy Hamed, Ngoc Thang Vu, Slim Abdennadher
In this paper, we first discuss the CS phenomenon in Egypt and the factors that gave rise to the current language.
Automatic Speech Recognition Automatic Speech Recognition (ASR) +2
no code implementations • WS 2019 • Xiang Yu, Agnieszka Falenska, Marina Haid, Ngoc Thang Vu, Jonas Kuhn
We introduce the IMS contribution to the Surface Realization Shared Task 2019.
no code implementations • WS 2019 • Xiang Yu, Agnieszka Falenska, Ngoc Thang Vu, Jonas Kuhn
We present a dependency tree linearization model with two novel components: (1) a tree-structured encoder based on bidirectional Tree-LSTM that propagates information first bottom-up then top-down, which allows each token to access information from the entire tree; and (2) a linguistically motivated head-first decoder that emphasizes the central role of the head and linearizes the subtree by incrementally attaching the dependents on both sides of the head.
no code implementations • 24 Sep 2019 • Injy Hamed, Moritz Zhu, Mohamed Elmahdy, Slim Abdennadher, Ngoc Thang Vu
Code-switching (CS) is a widespread phenomenon among bilingual and multilingual societies.
no code implementations • WS 2019 • Dirk V{\"a}th, Ngoc Thang Vu
In this paper, we explore state-of-the-art deep reinforcement learning methods for dialog policy training such as prioritized experience replay, double deep Q-Networks, dueling network architectures and distributional learning.
no code implementations • 13 Aug 2019 • Pavel Denisov, Ngoc Thang Vu
We present the IMS-Speech, a web based tool for German and English speech transcription aiming to facilitate research in various disciplines which require accesses to lexical information in spoken language materials.
Ranked #4 on Speech Recognition on TUDA (using extra training data)
no code implementations • 13 Aug 2019 • Pavel Denisov, Ngoc Thang Vu
This paper presents our latest investigation on end-to-end automatic speech recognition (ASR) for overlapped speech.
Automatic Speech Recognition Automatic Speech Recognition (ASR) +2
no code implementations • WS 2019 • Xiang Yu, Ngoc Thang Vu, Jonas Kuhn
The generalized Dyck language has been used to analyze the ability of Recurrent Neural Networks (RNNs) to learn context-free grammars (CFGs).
no code implementations • ACL 2019 • Daniel Ortega, Dirk V{\"a}th, Gianna Weber, V, Lindsey erlyn, Maximilian Schmidt, Moritz V{\"o}lkel, Zorica Karacevic, Ngoc Thang Vu
In this paper, we present ADVISER - an open source dialog system framework for education and research purposes.
no code implementations • 28 Feb 2019 • Daniel Ortega, Chia-Yu Li, Gisela Vallejo, Pavel Denisov, Ngoc Thang Vu
This paper presents our latest investigations on dialog act (DA) classification on automatically generated transcriptions.
Automatic Speech Recognition Automatic Speech Recognition (ASR) +4
no code implementations • WS 2018 • Xiang Yu, Ngoc Thang Vu, Jonas Kuhn
We present a general approach with reinforcement learning (RL) to approximate dynamic oracles for transition systems where exact dynamic oracles are difficult to derive.
no code implementations • WS 2018 • Glorianna Jagfeld, Sabrina Jenne, Ngoc Thang Vu
We present a comparison of word-based and character-based sequence-to-sequence models for data-to-text natural language generation, which generate natural language descriptions for structured inputs.
1 code implementation • CONLL 2018 • Matthias Blohm, Glorianna Jagfeld, Ekta Sood, Xiang Yu, Ngoc Thang Vu
We propose a machine reading comprehension model based on the compare-aggregate framework with two-staged attention that achieves state-of-the-art results on the MovieQA question answering dataset.
no code implementations • 10 Aug 2018 • Chia Yu Li, Ngoc Thang Vu
This paper presents our latest investigation on Densely Connected Convolutional Networks (DenseNets) for acoustic modelling (AM) in automatic speech recognition.
no code implementations • 30 Jul 2018 • Pavel Denisov, Ngoc Thang Vu, Marc Ferras Font
In this paper, we investigate the use of adversarial learning for unsupervised adaptation to unseen recording conditions, more specifically, single microphone far-field speech.
no code implementations • 13 Jul 2018 • Daniel Grießhaber, Ngoc Thang Vu, Johannes Maucher
Deep learning techniques have recently shown to be successful in many natural language processing tasks forming state-of-the-art systems.
no code implementations • WS 2018 • Sean Papay, Sebastian Pad{\'o}, Ngoc Thang Vu
Most modern approaches to computing word embeddings assume the availability of text corpora with billions of words.
no code implementations • 14 May 2018 • Sabrina Stehwien, Ngoc Thang Vu, Antje Schweitzer
Pitch accent detection often makes use of both acoustic and lexical features based on the fact that pitch accents tend to correlate with certain words.
no code implementations • 30 Apr 2018 • Michael Wand, Ngoc Thang Vu, Juergen Schmidhuber
Audiovisual speech recognition (AVSR) is a method to alleviate the adverse effect of noise in the acoustic signal.
no code implementations • NAACL 2018 • Kim Anh Nguyen, Sabine Schulte im Walde, Ngoc Thang Vu
We present two novel datasets for the low-resource language Vietnamese to assess models of semantic similarity: ViCon comprises pairs of synonyms and antonyms across word classes, thus offering data to distinguish between similarity and dissimilarity.
no code implementations • 2 Mar 2018 • Daniel Ortega, Ngoc Thang Vu
We propose a neural model that processes both lexical and acoustic features for classification.
no code implementations • 1 Mar 2018 • Michael Neumann, Ngoc Thang Vu
Research on multilingual speech emotion recognition faces the problem that most available speech corpora differ from each other in important ways, such as annotation methods or interaction scenarios.
no code implementations • 4 Oct 2017 • Heike Adel, Ngoc Thang Vu, Katrin Kirchhoff, Dominic Telaar, Tanja Schultz
The experimental results reveal that Brown word clusters, part-of-speech tags and open-class words are the most effective at reducing the perplexity of factored language models on the Mandarin-English Code-Switching corpus SEAME.
Automatic Speech Recognition Automatic Speech Recognition (ASR) +3
no code implementations • WS 2017 • Moritz Stiefel, Ngoc Thang Vu
Parsing speech requires a richer representation than 1-best or n-best hypotheses, e. g. lattices.
Automatic Speech Recognition Automatic Speech Recognition (ASR) +5
no code implementations • WS 2017 • Daniel Ortega, Ngoc Thang Vu
We explore context representation learning methods in neural-based models for dialog act classification.
no code implementations • WS 2017 • Ina Rösiger, Sabrina Stehwien, Arndt Riester, Ngoc Thang Vu
Adding manually annotated prosodic information, specifically pitch accents and phrasing, to the typical text-based feature set for coreference resolution has previously been shown to have a positive effect on German data.
no code implementations • EMNLP 2017 • Kim Anh Nguyen, Maximilian Köper, Sabine Schulte im Walde, Ngoc Thang Vu
We present a novel neural model HyperVec to learn hierarchical embeddings for hypernymy detection and directionality.
no code implementations • WS 2017 • Glorianna Jagfeld, Ngoc Thang Vu
This paper presents our novel method to encode word confusion networks, which can represent a rich hypothesis space of automatic speech recognition systems, via recurrent neural networks.
Automatic Speech Recognition Automatic Speech Recognition (ASR) +2
1 code implementation • WS 2017 • Xiang Yu, Agnieszka Faleńska, Ngoc Thang Vu
We present a general-purpose tagger based on convolutional neural networks (CNN), used for both composing word vectors and encoding context information.
no code implementations • 2 Jun 2017 • Sabrina Stehwien, Ngoc Thang Vu
This paper demonstrates the potential of convolutional neural networks (CNN) for detecting and classifying prosodic events on words, specifically pitch accents and phrase boundary tones, from frame-based acoustic features.
no code implementations • 2 Jun 2017 • Michael Neumann, Ngoc Thang Vu
Speech emotion recognition is an important and challenging task in the realm of human-computer interaction.
1 code implementation • ACL 2017 • Xiang Yu, Ngoc Thang Vu
We present a transition-based dependency parser that uses a convolutional neural network to compose word representations from characters.
1 code implementation • EACL 2017 • Kim Anh Nguyen, Sabine Schulte im Walde, Ngoc Thang Vu
Distinguishing between antonyms and synonyms is a key task to achieve high performance in NLP systems.
no code implementations • WS 2016 • Özlem Çetinoğlu, Sarah Schulz, Ngoc Thang Vu
This paper addresses challenges of Natural Language Processing (NLP) on non-canonical multilingual data in which two or more languages are mixed.
Automatic Speech Recognition Automatic Speech Recognition (ASR) +7
1 code implementation • COLING 2016 • Kim Anh Nguyen, Sabine Schulte im Walde, Ngoc Thang Vu
Word embeddings have been demonstrated to benefit NLP tasks impressively.
no code implementations • 24 Jun 2016 • Ngoc Thang Vu
We investigate the usage of convolutional neural networks (CNNs) for the slot filling task in spoken language understanding.
no code implementations • ACL 2016 • Kim Anh Nguyen, Sabine Schulte im Walde, Ngoc Thang Vu
We propose a novel vector representation that integrates lexical contrast into distributional vectors and strengthens the most salient features for determining degrees of word similarity.
no code implementations • NAACL 2016 • Ngoc Thang Vu, Heike Adel, Pankaj Gupta, Hinrich Schütze
This paper investigates two different neural architectures for the task of relation classification: convolutional neural networks and recurrent neural networks.