Seminars

Sep
23
Fri
Thamar Solorio (University of Houston) “Style Transfer for Data Augmentation in Sequence Labeling Tasks” @ Hackerman Hall B17
Sep 23 @ 12:00 pm – 1:15 pm

Abstract

I will present our work on data augmentation using style transfer as a way to improve domain adaptation in sequence labeling tasks. The target domain is social media data, and the task is named entity recognition (NER). The premise is that we can transform the labelled out of domain data into something that stylistically is more closely related to the target data. Then we can train a model on a combination of the generated data and the smaller amount of in domain data to improve NER prediction performance. I will show recent empirical results on these efforts.

If time allows, I will also give an overview of other research projects I’m currently leading at RiTUAL (Research in Text Understanding and Analysis of Language) lab. The common thread among all these research problems is the scarcity of labeled data.

Biography

Thamar Solorio is a Professor of Computer Science at the University of Houston (UH). She holds graduate degrees in Computer Science from the Instituto Nacional de Astrofísica, Óptica y Electrónica, in Puebla, Mexico. Her research interests include information extraction from social media data, enabling technology for code-switched data, stylistic modeling of text, and more recently multimodal approaches for online content understanding. She is the director and founder of the RiTUAL Lab at UH. She is the recipient of an NSF CAREER award for her work on authorship attribution, and recipient of the 2014 Emerging Leader ABIE Award in Honor of Denice Denton. She is currently serving a second term as an elected board member of the North American Chapter of the Association of Computational Linguistics and was PC co-chair for NAACL 2019. She recently joined the team of Editors in Chief for the ACL Rolling Review (ARR) system. Her research is currently funded by the NSF and by ADOBE.

Sep
30
Fri
Antonios Anastasopoulos (George Mason University) “NLP Beyond the Top-100 Languages” @ Hackerman Hall B17
Sep 30 @ 12:00 pm – 1:15 pm

Abstract

The availability of large multilingual pre-trained language models has opened up exciting pathways for developing NLP technologies for languages with scarce resources. In this talk I will advocate for the need to go beyond the most common languages in multilingual evaluation, and on the challenges of handling new, unseen-during-training languages and varieties. I will also share some of my experiences with working with indigenous and other endangered language communities and activists.

Biography

Antonios Anastasopoulos is an Assistant Professor in Computer Science at George Mason University. In 2019, Antonis received his PhD in Computer Science from the University of Notre Dame and then worked as a postdoctoral researcher at the Language Technologies Institute at Carnegie Mellon University. His research interests revolve around computational linguistics and natural language processing with a focus on low-resource settings, endangered languages, and cross-lingual learning.

 

Oct
3
Mon
Student Seminar @ Hackerman Hall B17
Oct 3 @ 12:00 pm – 1:15 pm
Oct
7
Fri
Ariya Rastrow (Amazon) @ Hackerman Hall B17
Oct 7 @ 12:00 pm – 1:15 pm
Oct
14
Fri
He He (New York University) “What We Talk about When We Talk about Spurious Correlations in NLP” @ Hackerman Hall B17
Oct 14 @ 12:00 pm – 1:15 pm

Abstract

Model robustness and spurious correlations have received increasing attention in the NLP community, both in methods and evaluation. The term “spurious correlation” is overloaded though and can refer to any undesirable shortcuts learned by the model, as judged by domain experts.

When designing mitigation algorithms, we often (implicitly) assume that a spurious feature is irrelevant for prediction. However, many features in NLP (e.g. word overlap and negation) are not spurious in the sense that the background is spurious for classifying objects in an image. In contrast, they carry important information that’s needed to make predictions by humans. In this talk, we argue that it is more productive to characterize features in terms of their necessity and sufficiency for prediction. We then discuss the implications of this categorization in representation, learning, and evaluation.

Biography

He He is an Assistant Professor in the Department of Computer Science and the Center for Data Science at New York University. She obtained her PhD in Computer Science at the University of Maryland, College Park. Before joining NYU, she spent a year at AWS AI and was a post-doc at Stanford University before that. She is interested in building robust and trustworthy NLP systems in human-centered settings. Her recent research focus includes robust language understanding, collaborative text generation, and understanding capabilities and issues of large language models.

Oct
17
Mon
David Chiang (University of Notre Dame) “Exact Recursive Probabilistic Programming with Colin McDonald, Darcey Riley, Kenneth Sible (Notre Dame) and Chung-chieh Shan (Indiana)” @ Hackerman Hall B17
Oct 17 @ 12:00 pm – 1:15 pm

Abstract

Recursive calls over recursive data are widely useful for generating probability distributions, and probabilistic programming allows computations over these distributions to be expressed in a modular and intuitive way. Exact inference is also useful, but unfortunately, existing probabilistic programming languages do not perform exact inference on recursive calls over recursive data, forcing programmers to code many applications manually. We introduce a probabilistic language in which a wide variety of recursion can be expressed naturally, and inference carried out exactly. For instance, probabilistic pushdown automata and their generalizations are easy to express, and polynomial-time parsing algorithms for them are derived automatically. We eliminate recursive data types using program transformations related to defunctionalization and refunctionalization. These transformations are assured correct by a linear type system, and a successful choice of transformations, if there is one, is guaranteed to be found by a greedy algorithm. I will also describe the implementation of this language in two phases: first, compilation to a factor graph grammar, and second, computing the sum-product of the factor graph grammar.
Biography
David Chiang (PhD, University of Pennsylvania, 2004) is an associate professor in the Department of Computer Science and Engineering at the University of Notre Dame. His research is on computational models for learning human languages, particularly how to translate from one language to another. His work on applying formal grammars and machine learning to translation has been recognized with two best paper awards (at ACL 2005 and NAACL HLT 2009). He has received research grants from DARPA, NSF, Google, and Amazon, has served on the executive board of NAACL and the editorial board of Computational Linguistics and JAIR, and is currently on the editorial board of Transactions of the ACL.
Oct
24
Mon
Fei Sha (University of Southern California) “Extracting Information from Text into Memory for Knowledge-Intensive Tasks” @ Hackerman Hall B17
Oct 24 @ 12:00 pm – 1:15 pm

Abstract

Modern learning architectures for natural language processing have been very successful in incorporating a huge amount of texts into their parameters. However, by and large, such models store and use knowledge in distributed and decentralized ways. This proves unreliable and makes the models ill-suited for knowledge-intensive tasks that require reasoning over factual information in linguistic expressions.  In this talk, I will give a few examples of exploring alternative architectures to tackle those challenges. In particular, we can improve the performance of such (language) models by representing, storing and accessing knowledge in a dedicated memory component.

This talk is based on several joint works with Yury Zemlyanskiy (Google Research), Michiel de Jong (USC and Google Research), William Cohen (Google Research and CMU) and our other collaborators in Google Research.

Biography

Fei is a research scientist at Google Research. Before that, he was a Professor of Computer Science at University of Southern California. His primary research interests are machine learning and its application to various AI problems: speech and language processing, computer vision, robotics and recently weather forecast and climate modeling.   He has a PhD (2007) from Computer and Information Science from U. of Pennsylvania and B.Sc and M.Sc in Biomedical Engineering from Southeast University (Nanjing, China).

Nov
4
Fri
Berrak Sisman (University of Texas at Dallas) “Speech Synthesis and Voice Conversion: Machine Learning can Mimic Anyone’s Voice” @ Hackerman Hall B17
Nov 4 @ 12:00 pm – 1:15 pm

Abstract

Voice conversion (VC) is a significant aspect of artificial intelligence. It is the study of how to convert one’s voice to sound like that of another without changing the linguistic content. Voice conversion belongs to a general technical field of speech synthesis, which converts text to speech or changes the properties of speech, for example, voice identity, emotion, and accents. Voice conversion involves multiple speech processing techniques, such as speech analysis, spectral conversion, prosody conversion, speaker characterization, and vocoding. With the recent advances in theory and practice, we are now able to produce human-like voice quality with high speaker similarity. In this talk, Dr. Sisman will present the recent advances in voice conversion and discuss their promise and limitations. Dr. Sisman will also provide a summary of the available resources for expressive voice conversion research.

Biography

Dr. Berrak Sisman (Member, IEEE) received the Ph.D. degree in electrical and computer engineering from National University of Singapore in 2020, fully funded by A*STAR Graduate Academy under Singapore International Graduate Award (SINGA). She is currently working as a tenure-track Assistant Professor at the Erik Jonsson School Department of Electrical and Computer Engineering at University of Texas at Dallas, United States. Prior to joining UT Dallas, she was a faculty member at Singapore University of Technology and Design (2020-2022). She was a Postdoctoral Research Fellow at the National University of Singapore (2019-2020). She was an exchange doctoral student at the University of Edinburgh and a visiting scholar at The Centre for Speech Technology Research (CSTR), University of Edinburgh (2019). She was a visiting researcher at RIKEN Advanced Intelligence Project in Japan (2018). Her research is focused on machine learning, signal processing, emotion, speech synthesis and voice conversion.

Dr. Sisman has served as the Area Chair at INTERSPEECH 2021, INTERSPEECH 2022, IEEE SLT 2022 and as the Publication Chair at ICASSP 2022. She has been elected as a member of the IEEE Speech and Language Processing Technical Committee (SLTC) in the area of Speech Synthesis for the term from January 2022 to December 2024. She plays leadership roles in conference organizations and active in technical committees. She has served as the General Coordinator of the Student Advisory Committee (SAC) of International Speech Communication Association (ISCA).

Nov
11
Fri
Hui Guan (University of Massachusetts Amherst) “Towards Accurate and Efficient Edge Computing Via Multi-Task Learning” @ Hackerman Hall B17
Nov 11 @ 12:00 pm – 1:15 pm

Abstract

AI-powered applications increasingly adopt Deep Neural Networks (DNNs) for solving many prediction tasks, leading to more than one DNNs running on resource-constrained devices. Supporting many models simultaneously on a device is challenging due to the linearly increased computation, energy, and storage costs. An effective approach to address the problem is multi-task learning (MTL) where a set of tasks are learned jointly to allow some parameter sharing among tasks. MTL creates multi-task models based on common DNN architectures and has shown significantly reduced inference costs and improved generalization performance in many machine learning applications. In this talk, we will introduce our recent efforts on leveraging MTL to improve accuracy and efficiency for edge computing. The talk will introduce multi-task architecture design systems that can automatically identify resource-efficient multi-task models with low inference costs and high task accuracy.
Biography
Hui Guan is an Assistant Professor in the College of Information and Computer Sciences (CICS) at the University of Massachusetts Amherst, the flagship campus of the UMass system. She received her Ph.D. in Electrical Engineering from North Carolina State University in 2020. Her research lies in the intersection between machine learning and systems, with an emphasis on improving the speed, scalability, and reliability of machine learning through innovations in algorithms and programming systems. Her current research focuses on both algorithm and system optimizations of deep multi-task learning and graph machine learning.
Nov
18
Fri
Angela Fan (Meta AI Research) “No Language Left Behind: Scaling Human-Centered Machine Translation” @ Hackerman Hall B17
Nov 18 @ 12:00 pm – 1:15 pm

Abstract

Driven by the goal of eradicating language barriers on a global scale, machine translation has solidified itself as a key focus of artificial intelligence research today. However, such efforts have coalesced around a small subset of languages, leaving behind the vast majority of mostly low-resource languages. What does it take to break the 200 language barrier while ensuring safe, high-quality results, all while keeping ethical considerations in mind? In this talk, I introduce No Language Left Behind, an initiative to break language barriers for low-resource languages. In No Language Left Behind, we took on the low-resource language translation challenge by first contextualizing the need for translation support through exploratory interviews with native speakers. Then, we created datasets and models aimed at narrowing the performance gap between low and high-resource languages. We proposed multiple architectural and training improvements to counteract overfitting while training on thousands of tasks. Critically, we evaluated the performance of over 40,000 different translation directions using a human-translated benchmark, Flores-200, and combined human evaluation with a novel toxicity benchmark covering all languages in Flores-200 to assess translation safety. Our model achieves an improvement of 44% BLEU relative to the previous state-of-the-art, laying important groundwork towards realizing a universal translation system in an open-source manner.

Biography

Angela is a research scientist at Meta AI Research in New York, focusing on supporting efforts in speech and language research. Recent projects include No Language Left Behind (https://ai.facebook.com/research/no-language-left-behind/) and Universal Speech Translation for Unwritten Languages (https://ai.facebook.com/blog/ai-translation-hokkien/). Before translation, Angela previously focused on research in on-device models for NLP and computer vision and text generation.

Center for Language and Speech Processing