Search Results for author: Alan W. black

Found 79 papers, 16 papers with code

Evaluating Gender Bias Transfer from Film Data

no code implementations NAACL (GeBNLP) 2022 Amanda Bertsch, Ashley Oh, Sanika Natu, Swetha Gangu, Alan W. black, Emma Strubell

We extend our analysis to a longitudinal study of bias in film dialogue over the last 110 years and find that continued pre-training on OpenSubtitles encodes additional bias into BERT.

Dialogue Generation Machine Translation +3

Nonlinear ISA with Auxiliary Variables for Learning Speech Representations

no code implementations25 Jul 2020 Amrith Setlur, Barnabas Poczos, Alan W. black

This paper extends recent work on nonlinear Independent Component Analysis (ICA) by introducing a theoretical framework for nonlinear Independent Subspace Analysis (ISA) in the presence of auxiliary variables.

Speaker Verification

Should You Fine-Tune BERT for Automated Essay Scoring?

no code implementations WS 2020 Elijah Mayfield, Alan W. black

Most natural language processing research now recommends large Transformer-based models with fine-tuning for supervised classification tasks; older strategies like bag-of-words features and linear models have fallen out of favor.

Automated Essay Scoring

ClarQ: A large-scale and diverse dataset for Clarification Question Generation

1 code implementation ACL 2020 Vaibhav Kumar, Alan W. black

In order to overcome these limitations, we devise a novel bootstrapping framework (based on self-supervision) that assists in the creation of a diverse, large-scale dataset of clarification questions based on post-comment tuples extracted from stackexchange.

Question Answering Question Generation +1

A Corpus for Large-Scale Phonetic Typology

no code implementations ACL 2020 Elizabeth Salesky, Eleanor Chodroff, Tiago Pimentel, Matthew Wiesner, Ryan Cotterell, Alan W. black, Jason Eisner

A major hurdle in data-driven research on typology is having sufficient data in many languages to draw meaningful conclusions.

Phone Features Improve Speech Translation

1 code implementation ACL 2020 Elizabeth Salesky, Alan W. black

End-to-end models for speech translation (ST) more tightly couple speech recognition (ASR) and machine translation (MT) than a traditional cascade of separate ASR and MT models, with simpler model architectures and the potential for reduced error propagation.

Machine Translation speech-recognition +2

Exploring Controllable Text Generation Techniques

no code implementations COLING 2020 Shrimai Prabhumoye, Alan W. black, Ruslan Salakhutdinov

In this work, we provide a new schema of the pipeline of the generation process by classifying it into five modules.

Text Generation

Style Variation as a Vantage Point for Code-Switching

no code implementations1 May 2020 Khyathi Raghavi Chandu, Alan W. black

We believe this viewpoint of CS as style variations opens new perspectives for modeling various tasks in CS text.

Language Modelling speech-recognition +3

Topological Sort for Sentence Ordering

2 code implementations ACL 2020 Shrimai Prabhumoye, Ruslan Salakhutdinov, Alan W. black

Sentence ordering is the task of arranging the sentences of a given text in the correct order.

Sentence Ordering

Politeness Transfer: A Tag and Generate Approach

2 code implementations ACL 2020 Aman Madaan, Amrith Setlur, Tanmay Parekh, Barnabas Poczos, Graham Neubig, Yiming Yang, Ruslan Salakhutdinov, Alan W. black, Shrimai Prabhumoye

This paper introduces a new task of politeness transfer which involves converting non-polite sentences to polite sentences while preserving the meaning.

Style Transfer TAG

AlloVera: A Multilingual Allophone Database

no code implementations LREC 2020 David R. Mortensen, Xinjian Li, Patrick Littell, Alexis Michaud, Shruti Rijhwani, Antonios Anastasopoulos, Alan W. black, Florian Metze, Graham Neubig

While phonemic representations are language specific, phonetic representations (stated in terms of (allo)phones) are much closer to a universal (language-independent) transcription.

speech-recognition Speech Recognition

Towards Zero-shot Learning for Automatic Phonemic Transcription

no code implementations26 Feb 2020 Xinjian Li, Siddharth Dalmia, David R. Mortensen, Juncheng Li, Alan W. black, Florian Metze

The difficulty of this task is that phoneme inventories often differ between the training languages and the target language, making it infeasible to recognize unseen phonemes.

Zero-Shot Learning

Towards Minimal Supervision BERT-based Grammar Error Correction

no code implementations10 Jan 2020 Yiyuan Li, Antonios Anastasopoulos, Alan W. black

Current grammatical error correction (GEC) models typically consider the task as sequence generation, which requires large amounts of annotated data and limit the applications in data-limited settings.

Grammatical Error Correction Language Modelling

A Resource for Computational Experiments on Mapudungun

1 code implementation LREC 2020 Mingjun Duan, Carlos Fasola, Sai Krishna Rallabandi, Rodolfo M. Vega, Antonios Anastasopoulos, Lori Levin, Alan W. black

We present a resource for computational experiments on Mapudungun, a polysynthetic indigenous language spoken in Chile with upwards of 200 thousand speakers.

Machine Translation speech-recognition +3

What A Sunny Day ☔: Toward Emoji-Sensitive Irony Detection

no code implementations WS 2019 Shirley Anugrah Hayati, Aditi Chaudhary, Naoki Otani, Alan W. black

Irony detection is an important task with applications in identification of online abuse and harassment.

Multimodal, Multilingual Grapheme-to-Phoneme Conversion for Low-Resource Languages

no code implementations WS 2019 James Route, Steven Hillis, Isak Czeresnia Etinger, Han Zhang, Alan W. black

Grapheme-to-phoneme conversion (g2p) is the task of predicting the pronunciation of words from their orthographic representation.


Learning to Order Graph Elements with Application to Multilingual Surface Realization

no code implementations WS 2019 Wenchao Du, Alan W. black

Recent advances in deep learning have shown promises in solving complex combinatorial optimization problems, such as sorting variable-sized sequences.

Combinatorial Optimization

A Dynamic Strategy Coach for Effective Negotiation

no code implementations WS 2019 Yiheng Zhou, He He, Alan W. black, Yulia Tsvetkov

We consider a bargaining scenario where a seller and a buyer negotiate the price of an item for sale through a text-based dialog.

Decision Making Text Generation

Induction and Reference of Entities in a Visual Story

no code implementations15 Sep 2019 Ruo-Ping Dong, Khyathi Raghavi Chandu, Alan W. black

We also conduct human evaluation from which it is concluded that the visual stories generated by our model are preferred 82% of the times.

Visual Storytelling

CMU GetGoing: An Understandable and Memorable Dialog System for Seniors

no code implementations3 Sep 2019 Shikib Mehri, Alan W. black, Maxine Eskenazi

Voice-based technologies are typically developed for the average user, and thus generally not tailored to the specific needs of any subgroup of the population, like seniors.

Linguistic Versus Latent Relations for Modeling Coherent Flow in Paragraphs

1 code implementation IJCNLP 2019 Dongyeop Kang, Hiroaki Hayashi, Alan W. black, Eduard Hovy

In order to produce a coherent flow of text, we explore two forms of intersentential relations in a paragraph: one is a human-created linguistical relation that forms a structure (e. g., discourse tree) and the other is a relation from latent representation learned from the sentences themselves.

Language Modelling

Multilingual Speech Recognition with Corpus Relatedness Sampling

no code implementations2 Aug 2019 Xinjian Li, Siddharth Dalmia, Alan W. black, Florian Metze

For example, the target corpus might benefit more from a corpus in the same domain or a corpus from a close language.

speech-recognition Speech Recognition

``My Way of Telling a Story'': Persona based Grounded Story Generation

no code implementations WS 2019 Ch, Khyathi u, Shrimai Prabhumoye, Ruslan Salakhutdinov, Alan W. black

To this end, we propose five models which are incremental extensions to the baseline model to perform the task at hand.

Visual Storytelling

WriterForcing: Generating more interesting story endings

2 code implementations WS 2019 Prakhar Gupta, Vinayshekhar Bannihatti Kumar, Mukul Bhutani, Alan W. black

In this paper, we propose models which generate more diverse and interesting outputs by 1) training models to focus attention on important keyphrases of the story, and 2) promoting generation of non-generic words.

Text Generation

Boosting Dialog Response Generation

no code implementations ACL 2019 Wenchao Du, Alan W. black

Neural models have become one of the most important approaches to dialog response generation.

Response Generation

Measuring Bias in Contextualized Word Representations

1 code implementation WS 2019 Keita Kurita, Nidhi Vyas, Ayush Pareek, Alan W. black, Yulia Tsvetkov

Contextual word embeddings such as BERT have achieved state of the art performance in numerous NLP tasks.

Word Embeddings

"My Way of Telling a Story": Persona based Grounded Story Generation

no code implementations14 Jun 2019 Shrimai Prabhumoye, Khyathi Raghavi Chandu, Ruslan Salakhutdinov, Alan W. black

To this end, we propose five models which are incremental extensions to the baseline model to perform the task at hand.

Visual Storytelling

Exploring Phoneme-Level Speech Representations for End-to-End Speech Translation

no code implementations ACL 2019 Elizabeth Salesky, Matthias Sperber, Alan W. black

Previous work on end-to-end translation from speech has primarily used frame-level features as speech representations, which creates longer, sparser sequences than text.


Top-Down Structurally-Constrained Neural Response Generation with Lexicalized Probabilistic Context-Free Grammar

no code implementations NAACL 2019 Wenchao Du, Alan W. black

We consider neural language generation under a novel problem setting: generating the words of a sentence according to the order of their first appearance in its lexicalized PCFG parse tree, in a depth-first, left-to-right manner.

Response Generation Text Generation

The Zero Resource Speech Challenge 2019: TTS without T

no code implementations25 Apr 2019 Ewan Dunbar, Robin Algayres, Julien Karadayi, Mathieu Bernard, Juan Benjumea, Xuan-Nga Cao, Lucie Miskic, Charlotte Dugrain, Lucas Ondel, Alan W. black, Laurent Besacier, Sakriani Sakti, Emmanuel Dupoux

We present the Zero Resource Speech Challenge 2019, which proposes to build a speech synthesizer without any text or phonetic labels: hence, TTS without T (text-to-speech without text).

A Survey of Code-switched Speech and Language Processing

no code implementations25 Mar 2019 Sunayana Sitaram, Khyathi Raghavi Chandu, Sai Krishna Rallabandi, Alan W. black

Code-switching, the alternation of languages within a conversation or utterance, is a common communicative phenomenon that occurs in multilingual communities across the world.

The ARIEL-CMU Systems for LoReHLT18

no code implementations24 Feb 2019 Aditi Chaudhary, Siddharth Dalmia, Junjie Hu, Xinjian Li, Austin Matthews, Aldrian Obaja Muis, Naoki Otani, Shruti Rijhwani, Zaid Sheikh, Nidhi Vyas, Xinyi Wang, Jiateng Xie, Ruochen Xu, Chunting Zhou, Peter J. Jansen, Yiming Yang, Lori Levin, Florian Metze, Teruko Mitamura, David R. Mortensen, Graham Neubig, Eduard Hovy, Alan W. black, Jaime Carbonell, Graham V. Horwood, Shabnam Tafreshi, Mona Diab, Efsun S. Kayi, Noura Farra, Kathleen McKeown

This paper describes the ARIEL-CMU submissions to the Low Resource Human Language Technologies (LoReHLT) 2018 evaluations for the tasks Machine Translation (MT), Entity Discovery and Linking (EDL), and detection of Situation Frames in Text and Speech (SF Text and Speech).

Machine Translation Translation

Phoneme Level Language Models for Sequence Based Low Resource ASR

no code implementations20 Feb 2019 Siddharth Dalmia, Xinjian Li, Alan W. black, Florian Metze

Building multilingual and crosslingual models help bring different languages together in a language universal space.

Language Modelling

A Dataset for Document Grounded Conversations

3 code implementations EMNLP 2018 Kangyan Zhou, Shrimai Prabhumoye, Alan W. black

We define "Document Grounded Conversations" as conversations that are about the contents of a specified document.

Style Transfer Through Multilingual and Feedback-Based Back-Translation

no code implementations17 Sep 2018 Shrimai Prabhumoye, Yulia Tsvetkov, Alan W. black, Ruslan Salakhutdinov

Style transfer is the task of transferring an attribute of a sentence (e. g., formality) while maintaining its semantic content.

Style Transfer Translation

Data Augmentation for Neural Online Chat Response Selection

no code implementations3 Sep 2018 Wenchao Du, Alan W. black

Data augmentation seeks to manipulate the available data for training to improve the generalization ability of models.

Data Augmentation

Domain Robust Feature Extraction for Rapid Low Resource ASR Development

no code implementations28 Jul 2018 Siddharth Dalmia, Xinjian Li, Florian Metze, Alan W. black

We demonstrate the effectiveness of using a pre-trained English recognizer, which is robust to such mismatched conditions, as a domain normalizing feature extractor on a low resource language.


Generating Mandarin and Cantonese F0 Contours with Decision Trees and BLSTMs

no code implementations4 Jul 2018 Weidong Yuan, Alan W. black

This paper models the fundamental frequency contours on both Mandarin and Cantonese speech with decision trees and DNNs (deep neural networks).

DialCrowd: A toolkit for easy dialog system assessment

no code implementations WS 2018 Kyusong Lee, Tiancheng Zhao, Alan W. black, Maxine Eskenazi

When creating a dialog system, developers need to test each version to ensure that it is performing correctly.

Chatbot Test

Code-Mixed Question Answering Challenge: Crowd-sourcing Data and Techniques

no code implementations WS 2018 Ch, Khyathi u, Ekaterina Loginova, Vishal Gupta, Josef van Genabith, G{\"u}nter Neumann, Manoj Chinnakotla, Eric Nyberg, Alan W. black

As a first step towards fostering research which supports CM in NLP applications, we systematically crowd-sourced and curated an evaluation dataset for factoid question answering in three CM languages - Hinglish (Hindi+English), Tenglish (Telugu+English) and Tamlish (Tamil+English) which belong to two language families (Indo-Aryan and Dravidian).

Question Answering

Tackling Code-Switched NER: Participation of CMU

no code implementations WS 2018 Parvathy Geetha, Ch, Khyathi u, Alan W. black

In this paper we describe models that intuitively developed from the data for the shared task Named Entity Recognition on Code-switched Data.

named-entity-recognition Named Entity Recognition +3

Language Informed Modeling of Code-Switched Text

no code implementations WS 2018 Ch, Khyathi u, Thomas Manzini, Sumeet Singh, Alan W. black

Code-switching (CS), the practice of alternating between two or more languages in conversations, is pervasive in most multi-lingual communities.

Language Modelling Machine Translation +2

An Empirical Study of Self-Disclosure in Spoken Dialogue Systems

no code implementations WS 2018 Ravich, Abhilasha er, Alan W. Black

Self-disclosure is a key social strategy employed in conversation to build relations and increase conversational depth.

Spoken Dialogue Systems

Automatic Detection of Code-switching Style from Acoustics

no code implementations WS 2018 Rallab, SaiKrishna i, Sunayana Sitaram, Alan W. black

We hypothesize that it may be useful for an ASR system to be able to first detect the switching style of a particular utterance from acoustics, and then use specialized language models or other adaptation techniques for decoding the speech.

Automatic Speech Recognition (ASR) Language Identification +1

Style Transfer Through Back-Translation

3 code implementations ACL 2018 Shrimai Prabhumoye, Yulia Tsvetkov, Ruslan Salakhutdinov, Alan W. black

We first learn a latent representation of the input sentence which is grounded in a language translation model in order to better preserve the meaning of the sentence while reducing stylistic properties.

Style Transfer Text Style Transfer +1

Sequence-based Multi-lingual Low Resource Speech Recognition

no code implementations21 Feb 2018 Siddharth Dalmia, Ramon Sanabria, Florian Metze, Alan W. black

Techniques for multi-lingual and cross-lingual speech recognition can help in low resource scenarios, to bootstrap systems and enable analysis of new languages and domains.

speech-recognition Speech Recognition

Learning Conversational Systems that Interleave Task and Non-Task Content

no code implementations1 Mar 2017 Zhou Yu, Alan W. black, Alexander I. Rudnicky

These systems work well when users have clear and explicit intentions that are well-aligned to the systems' capabilities.


Polyglot Neural Language Models: A Case Study in Cross-Lingual Phonetic Representation Learning

no code implementations NAACL 2016 Yulia Tsvetkov, Sunayana Sitaram, Manaal Faruqui, Guillaume Lample, Patrick Littell, David Mortensen, Alan W. black, Lori Levin, Chris Dyer

We introduce polyglot language models, recurrent neural network models trained to predict symbol sequences in many different languages using shared representations of symbols and conditioning on typological information about the language to be predicted.

Representation Learning

Speech Synthesis of Code-Mixed Text

no code implementations LREC 2016 Sunayana Sitaram, Alan W. black

Most Text to Speech (TTS) systems today assume that the input text is in a single language and is written in the same language that the text needs to be synthesized in.

Language Identification Speech Synthesis

Recurrent Neural Network Postfilters for Statistical Parametric Speech Synthesis

no code implementations26 Jan 2016 Prasanna Kumar Muthukumar, Alan W. black

In the last two years, there have been numerous papers that have looked into using Deep Neural Networks to replace the acoustic model in traditional statistical parametric speech synthesis.

General Classification regression +1

Character-based Neural Machine Translation

no code implementations14 Nov 2015 Wang Ling, Isabel Trancoso, Chris Dyer, Alan W. black

We introduce a neural machine translation model that views the input and output sentences as sequences of characters rather than words.

Machine Translation Translation

A Deep Learning Approach to Data-driven Parameterizations for Statistical Parametric Speech Synthesis

no code implementations30 Sep 2014 Prasanna Kumar Muthukumar, Alan W. black

Mel Cepstral coefficients were never intended to work in a parametric speech synthesis framework, but as yet, there has been little success in creating a better parameterization that is more suited to synthesis.

Denoising Speech Synthesis

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