Publications

Jason Hartford

Kartik Ahuja

Christian Shewmake

Simone Azeglio

Arianna Di Bernardo

Nina Miolane

Extended Abstract Track

Jason Hartford

Kartik Ahuja

Christian Shewmake

Simone Azeglio

Arianna Di Bernardo

Nina Miolane

Extended Abstract Track

Jason Hartford

Sophia Sanborn

Christian Shewmake

Simone Azeglio

Arianna Di Bernardo

Nina Miolane

Extended Abstract Track

Jason Hartford

Kartik Ahuja

Christian Shewmake

Simone Azeglio

Arianna Di Bernardo

Nina Miolane

There has been significant recent progress in causal representation learning that has showed a variety of settings in which we can disentang… (voir plus)le latent variables with identifiability guarantees (up to some reasonable equivalence class). Common to all of these approaches is the assumption that (1) the latent variables are d − dimensional vectors, and (2) that the observations are the output of some injective observation function of these latent variables. While these assumptions appear benign—they amount to assuming that any changes in the latent space are reflected in the observation space, and that we can use standard encoders to infer the latent variables—we show that when the observations are of multiple objects, the observation function is no longer injective, and disentanglement fails in practice. We can address this failure by combining recent developments in object-centric learning and causal representation learning. By modifying the Slot Attention architecture (Locatello et al., 2020b), we develop an object-centric architecture that leverages weak supervision from sparse perturbations to disentangle each object’s properties. We argue that this approach is more data-efficient in the sense that it requires significantly fewer perturbations than a comparable approach that encodes to a Euclidean space and, we show that this approach successfully disentangles the properties of a set of objects in a series of simple image-based disentanglement experiments.

2022-01-01

(publié)

www.semanticscholar.org

Extracting Person Names from User Generated Text: Named-Entity Recognition for Combating Human Trafficking

Yifei Li

Pratheeksha Nair

Kellin Pelrine

Reihaneh Rabbany

Online escort advertisement websites are widely used for advertising victims of human trafficking. Domain experts agree that advertising mul… (voir plus)tiple people in the same ad is a strong indicator of trafficking. Thus, extracting person names from the text of these ads can provide valuable clues for further analysis. However, Named-Entity Recognition (NER) on escort ads is challenging because the text can be noisy, colloquial and often lacking proper grammar and punctuation. Most existing state-of-the-art NER models fail to demonstrate satisfactory performance in this task. In this paper, we propose NEAT (Name Extraction Against Trafficking) for extracting person names. It effectively combines classic rule-based and dictionary extractors with a contextualized language model to capture ambiguous names (e.g penny, hazel) and adapts to adversarial changes in the text by expanding its dictionary. NEAT shows 19% improvement on average in the F1 classification score for name extraction compared to previous state-of-the-art in two domain-specific datasets.

2022-01-01

Findings (published)

Extracting Person Names from User Generated Text: Named-Entity Recognition for Combating Human Trafficking

Yifei Li

Pratheeksha Nair

Kellin Pelrine

Reihaneh Rabbany

2022-01-01

Findings (publié)

Feeding What You Need by Understanding What You Learned

Xiaoqiang Wang

Bang Liu

Fangli Xu

Bo Long

Siliang Tang

Lingfei Wu

2022-01-01

ACL (1) (publié)

arxiv.org

Few-Shot Pidgin Text Adaptation via Contrastive Fine-Tuning

Ernie Chang

Jesujoba Oluwadara Alabi

David Ifeoluwa Adelani

Vera Demberg

The surging demand for multilingual dialogue systems often requires a costly labeling process for each language addition. For low resource l… (voir plus)anguages, human annotators are continuously tasked with the adaptation of resource-rich language utterances for each new domain. However, this prohibitive and impractical process can often be a bottleneck for low resource languages that are still without proper translation systems nor parallel corpus. In particular, it is difficult to obtain task-specific low resource language annotations for the English-derived creoles (e.g. Nigerian and Cameroonian Pidgin). To address this issue, we utilize the pretrained language models i.e. BART which has shown great potential in language generation/understanding – we propose to finetune the BART model to generate utterances in Pidgin by leveraging the proximity of the source and target languages, and utilizing positive and negative examples in constrastive training objectives. We collected and released the first parallel Pidgin-English conversation corpus in two dialogue domains and showed that this simple and effective technique is suffice to yield impressive results for English-to-Pidgin generation, which are two closely-related languages.

2022-01-01

COLING (publié)

dblp.uni-trier.de

Findings of the WMT’22 Shared Task on Large-Scale Machine Translation Evaluation for African Languages

David Ifeoluwa Adelani

Md Mahfuz Ibn Alam

Antonios Anastasopoulos

Akshita Bhagia

Marta R. Costa-jussa

Jesse Dodge

Fahim Faisal

Christian Federmann

Natalia N. Fedorova

Francisco S. Guzm'an

Sergey Koshelev

Jean Maillard

Vukosi Marivate

Jonathan Mbuya

Alexandre Mourachko

Safiyyah Saleem

Holger Schwenk

Guillaume Wenzek

We present the results of the WMT’22 SharedTask on Large-Scale Machine Translation Evaluation for African Languages. The shared taskinclud… (voir plus)ed both a data and a systems track, alongwith additional innovations, such as a focus onAfrican languages and extensive human evaluation of submitted systems. We received 14system submissions from 8 teams, as well as6 data track contributions. We report a largeprogress in the quality of translation for Africanlanguages since the last iteration of this sharedtask: there is an increase of about 7.5 BLEUpoints across 72 language pairs, and the average BLEU scores went from 15.09 to 22.60.

2022-01-01

Conference on Machine Translation (publié)

dblp.uni-trier.de

Flexible Diffusion Modeling of Long Videos

William Harvey

Saeid Naderiparizi

Vaden Masrani

Christian Dietrich Weilbach

Frank N. Wood

We present a framework for video modeling based on denoising diffusion probabilistic models that produces long-duration video completions in… (voir plus) a variety of realistic environments. We introduce a generative model that can at test-time sample any arbitrary subset of video frames conditioned on any other subset and present an architecture adapted for this purpose. Doing so allows us to efficiently compare and optimize a variety of schedules for the order in which frames in a long video are sampled and use selective sparse and long-range conditioning on previously sampled frames. We demonstrate improved video modeling over prior work on a number of datasets and sample temporally coherent videos over 25 minutes in length. We additionally release a new video modeling dataset and semantically meaningful metrics based on videos generated in the CARLA autonomous driving simulator.

openreview.net

S5 Framework: A Review of Self-Supervised Shared Semantic Space Optimization for Multimodal Zero-Shot Learning

Clst

Yonatan Bisk

Ari Holtzman

Jesse Thomason

Ja-740 cob

Joyce Chai

Angeliki Lapata

Jonathan Lazaridou

Alek-742 May

Nicolas sandr Nisnevich

P. PintoJoseph

Turian

Ting Chen

Simon Kornblith

Mohammad Norouzi

Yen-Chun Chen

Linjie Li

Licheng Yu

Ahmed El … (voir 89 de plus)

Faisal Kholy

Zhe Ahmed

Yu Gan

Cheng

Zihan Dai

Hanxiao Liu

Quoc V. Le

Jia Deng

Wei Dong

Richard Socher

Li-Jia Li

K. Liu

Jacob Devlin

Ming-Wei Chang

Kenton Lee

Jesse Dodge

Maarten Sap

Ana Marasovic

Gabriel Agnew

Dirk Ilharco

Groeneveld Matt

Li Dong

Nan Yang

Wenhui Wang

Furu Wei

Yu Liu

Jianfeng Wang

Ming Gao

Zhou

Xiaoyi Dong

Jia Bao

Ting Zhang

Dongdong

Weiming Chen

Lu Zhang

Dong Yuan

Fang Chen

Da-cheng Juan

Chuntian Lu

Zhen Li

Futang Peng

Aleksei Timofeev

Yi-Ting Chen

Yaxi Gao

Tom

Andrew Duerig

Tomkins Sujith

Ravi

Lukasz Kaiser

Aidan N. Gomez

Noam M. Shazeer

Niki Vaswani

Llion Parmar

Jones Jakob

Uszko-850

Alex G. Kendall

Yarin Gal

Roberto Cipolla

Salman H. Khan

Muzammal Naseer

Munawar Hayat

Waqas Zamir

Fahad Shahbaz

Khan

Ranjay Krishna

Yuke Zhu

Oliver Groth

Justin John-867

Kenji Hata

Joshua Kravitz

Stephanie Chen

Mike Lewis

Yinhan Liu

Marjan Naman Goyal

Abdelrahman Ghazvininejad

Omer Mohamed

Levy

Luke Zettlemoyer

Bohan Li

Hao Zhou

Jun-Tao He

Mingxuan Wang

Liunian Harold

Mark Li

Da Yatskar

Yin

Cho-Jui

Kai-Wei Chang

Visualbert

In this review, we aim to inspire research into 001 S elf-S upervised S hared S emantic S pace ( S5 ) 002 multimodal learning problems. We e… (voir plus)quip non-003 expert researchers with a framework of in-004 formed modeling decisions via an extensive 005 literature review, an actionable modeling check-006 list, as well as a series of novel zero-shot eval-007 uation tasks. The core idea for our S5 check-008 list lies in learning contextual multimodal in-009 teractions at various granularity levels via a 010 shared Transformer encoder with a denoising 011 loss term, which is also regularized by a con-012 trastive loss term to induce a semantic align-013 ment prior on the contextual embedding space. 014 Essentially, we aim to model human concept 015 understanding and thus learn to “put a name to 016 a face”. This ultimately enables interpretable 017 zero-shot S5 generalization on a variety of 018 novel downstream tasks. In summary, this re-019 view provides sufficient background and ac-020 tionable strategies for training cutting-edge S5 021 multimodal networks. 022

GANSpiration: Balancing Targeted and Serendipitous Inspiration in User Interface Design with Style-Based Generative Adversarial Network

Mohammad Amin Mozaffari

Xinyuan Zhang

Jinghui Cheng

Jin Guo

Inspiration from design examples plays a crucial role in the creative process of user interface design. However, current tools and technique… (voir plus)s that support inspiration usually only focus on example browsing with limited user control or similarity-based example retrieval, leading to undesirable design outcomes such as focus drift and design fixation. To address these issues, we propose the GANSpiration approach that suggests design examples for both targeted and serendipitous inspiration, leveraging a style-based Generative Adversarial Network. A quantitative evaluation revealed that the outputs of GANSpiration-based example suggestion approaches are relevant to the input design, and at the same time include diverse instances. A user study with professional UI/UX practitioners showed that the examples suggested by our approach serve as viable sources of inspiration for overall design concepts and specific design elements. Overall, our work paves the road of using advanced generative machine learning techniques in supporting the creative design practice.

2022-01-01

CHI (publié)