Publications

Extended Abstract Track
Amin Mansouri
Jason Hartford
Yoshua Bengio
Sophia Sanborn
Christian Shewmake
Simone Azeglio
Arianna Di Bernardo
Nina Miolane
Extended Abstract Track
Amin Mansouri
Jason Hartford
Kartik Ahuja
Yoshua Bengio
Christian Shewmake
Simone Azeglio
Arianna Di Bernardo
Nina Miolane
Extended Abstract Track
Amin Mansouri
Jason Hartford
Kartik Ahuja
Yoshua Bengio
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… (see more)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
(published)
www.semanticscholar.org
Extended Abstract Track
Amin Mansouri
Jason Hartford
Kartik Ahuja
Yoshua Bengio
Christian Shewmake
Simone Azeglio
Arianna Di Bernardo
Nina Miolane
Extended Abstract Track
Amin Mansouri
Jason Hartford
Kartik Ahuja
Yoshua Bengio
Christian Shewmake
Simone Azeglio
Arianna Di Bernardo
Nina Miolane
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… (see more)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)
doi.org
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 (published)
doi.org
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) (published)
doi.org
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… (see more)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 (published)
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… (see more)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 (published)
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… (see more) 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
Yoshua Bengio
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 … (see 89 more)
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
Tinglu 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… (see more)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