Natural Language Processing

Friday, January 19, 2018 - 10:30

Speaker: Alexander M. Rush, Harvard University
Location: CSE 305

Monday, January 8, 2018 - 10:30

Speaker: Chris Dyer, Deepmind
Location: CSE 305

Friday, September 29, 2017 - 10:30

Speaker: Margaret Mitchell, Google Seattle
Location: CSE 305

Monday, April 3, 2017 - 10:00

Speaker: Doug Oard (University of Maryland)
Location: EEB 303
The usual setup for Knowledge Base Population (KBP) is that we are initially given some collection of documents and (optionally) some incomplete knowledge base, and we are asked to produce a more complete knowledge base in which the set of entities, the attributes of those entities, and the relationships between those entities have been enriched based on information attested in the document collection. In this talk, we will describe our work with two prerequisites for KBP for content produced informally through conversation, as for example often happens in speech or email. One thing we would like to know if we are to update the right entity representations is who or what is being mentioned; this is an entity linking task. We’ll therefore begin our talk by describing our experiments with KBP from email, describing our work both with the well known Enron email collection and with the more recently created Avocado email collection that is now available for research use from LDC. One result of that reseach is that social features such as who is mentioning someone to whom are particularly useful. For that reason, we will next step back a level and consider the speaker identification problem in conversational telephone speech. Working with a set of more than one thousand recorded phone calls made or received by Enron energy traders, we have explored the use of two types of information from our Enron email knowledge base, and some additional social and channel features, to improve over the speaker recognition accuracy that we could achieve using only acoustic features. We’ll conclude the talk with a look ahead to see what needs to be done to bring these and other components together to actually populate knowledge bases from conversational sources. This is joint work with Tamer Elsayed, Mark Dredze and Greg Sell. Bio: Douglas Oard is a Professor at the University of Maryland, College Park, with joint appointments in the College of Information Studies and the University of Maryland Institute for Advanced Computer Studies (UMIACS). Dr. Oard earned his Ph.D. in Electrical Engineering from the University of Maryland. His research interests center around the use of emerging technologies to support information seeking by end users. Additional information is available at

Monday, February 27, 2017 - 10:00

Speaker: Yulia Tsvetkov (Stanford)
Location: CSE 305
One way to build more powerful, robust models and to provide deeper insight into data is to build hybrid models, integrating linguistic/social signals into statistical learning. I'll present model-based approaches that incorporate linguistic and social diversity into deep learning models to make them more robust and less biased toward particular languages, varieties, or demographics. First, I'll describe polyglot language models: recurrent neural networks that use shared annotations and representations in a single computational analyzer to process multiple languages, for the benefit of all, in particular those with insufficient training resources. Then, I’ll present an approach to integrating linguistic diversity into training data of non-convex models. The method optimizes linguistic content and structure of available training data to find a better curriculum for learning distributed representations of words. I’ll conclude with an overview of my current research which focuses on socially equitable NLP models: adversarial models that incorporate social diversity in the training objective to eliminate social biases hidden in data. Bio: Yulia Tsvetkov is a postdoc in the Stanford NLP Group, where she works on with professor Dan Jurafsky on NLP for social good. During her PhD in the Language Technologies Institute at Carnegie Mellon University, she worked on advancing machine learning techniques to tackle cross-lingual and cross-domain problems in natural language processing, focusing on computational phonology and morphology, distributional and lexical semantics, and statistical machine translation of both text and speech. In 2017, Yulia will join the Language Technologies Institute at CMU as an assistant professor.

Monday, February 13, 2017 - 10:00

Speaker: Julia Hockenmaier (UIUC)
Location: CSE 305
The task of automatic image captioning (i.e. associating images with sentences that describe what is depicted in them) has received a lot of attention over the last couple of years. But how well do these systems actually work? What are the limits of current approaches? In this talk, I will attempt to give an overview of how work in this area has developed. I will also highlight some shortcomings of current approaches, and discuss future directions. Bio: Julia Hockenmaier is associate professor in Computer Science at the University of Illinois at Urbana-Champaign. She works on natural language processing. Her current research focuses on automatic image description, statistical parsing and unsupervised grammar induction. Her group produced the popular Flickr30K dataset. She has given tutorials on image description at EACL and CVPR. Julia received her PhD from the University of Edinburgh and did postdoctoral work at the University of Pennsylvania. She has received an NSF CAREER award was shortlisted for the British Computer Society’s Distinguished Dissertation award.

Monday, January 23, 2017 - 10:00

Speaker: Hal Daume (University of Maryland)
Location: CSE 305
Machine learning-based natural language processing systems are amazingly effective, when plentiful labeled training data exists for the task/domain of interest. Unfortunately, for broad coverage (both in task and domain) language understanding, we're unlikely to ever have sufficient labeled data, and systems must find some other way to learn. I'll describe a novel algorithm for learning from interactions, and several problems of interest, most notably machine simultaneous interpretation (translation while someone is still speaking). This is all joint work with some amazing (former) students He He, Alvin Grissom II, John Morgan, Mohit Iyyer, Sudha Rao and Leonardo Claudino, as well as colleagues Jordan Boyd-Graber, Kai-Wei Chang, John Langford, Akshay Krishnamurthy, Alekh Agarwal, Stéphane Ross, Alina Beygelzimer and Paul Mineiro. Bio: Hal Daume III is an associate professor in Computer Science at the University of Maryland, College Park. He holds joint appointments in UMIACS and Linguistics. He has recieved best paper awards at NAACL 2016, CEAS 2010 and ECML 2008, and a best demonstration award at NIPS 2015. He was an executive board member of the North American Association for Computational Linguistics and then, in 2013, one of two program co-chairs for its conference (NAACL), and was previously the chair of the NAACL executive board. He has served as an editor for the Machine Learning Journal, the Computational Linguistics Journal and the Journal for Artificial Intelligence Research. His primary research interest is in developing new learning algorithms for prototypical problems that arise in the context of language processing and artificial intelligence. This includes topics like structured prediction, domain adaptation and unsupervised learning; as well as multilingual modeling and affect analysis. He earned his PhD at the University of Southern California with a thesis on structured prediction for language (his advisor was Daniel Marcu).

Monday, December 5, 2016 - 10:00

Speaker: Martha Palmer (University of Colorado)
Location: Gates Commons (CSE 691)
Abstract Meaning Representations (AMRs) provide a single, graph-based semantic representation that abstracts away from the word order and syntactic structure of a sentence, resulting in a more language-neutral representation of its meaning. AMRs implements a simplified, standard neo-Davidsonian semantics. A word in a sentence either maps to a concept or a relation or is omitted if it is already inherent in the representation or it conveys inter-personal attitude (e.g., stance or distancing). The basis of AMR is PropBank’s lexicon of coarse-grained senses of verb, noun and adjective relations as well as the roles associated with each sense (each lexicon entry is a ‘roleset’). By marking the appropriate roles for each sense, this level of annotation provides information regarding who is doing what to whom. However, unlike PropBank, AMR also provides a deeper level of representation of discourse relations, non-relational noun phrases, prepositional phrases, quantities and time expressions (which PropBank largely leaves unanalyzed), as well as Named Entity tags with Wikipedia links. Additionally, AMR makes a greater effort to abstract away from language-particular syntactic facts. The latest version of AMR includes adding coreference links across sentences, including links to implicit arguments. This talk will explore the differences between PropBank and AMR, the current and future plans for AMR annotation, and the potential of AMR as a basis for machine translation. It will end with a discussion of areas of semantic representation that AMR is not currently addressing, which remain as open challenges. Martha Palmer is a Professor at the University of Colorado in Linguistics, Computer Science and Cognitive Science, and a Fellow of the Association of Computational Linguistics.. She works on trying to capture elements of the meanings of words that can comprise automatic representations of complex sentences and documents. Supervised machine learning techniques rely on vast amounts of annotated training data so she and her students are engaged in providing data with word sense tags, semantic role labels and AMRs for English, Chinese, Arabic, Hindi, and Urdu, both manually and automatically, funded by DARPA and NSF. These methods have also recently been applied to biomedical journal articles, clinical notes, and geo-science documents, funded by NIH and NSF. She is a co-editor of LiLT, Linguistic Issues in Language Technology, and has been on the CLJ Editorial Board and a co-editor of JNLE. She is a past President of the Association for Computational Linguistics, past Chair of SIGLEX and SIGHAN, co-organizer of the first few Sensevals, and was the Director of the 2011 Linguistics Institute held in Boulder, Colorado.

Monday, November 14, 2016 - 10:00

Speaker: Reut Tsarfaty (Open University of Israel)
Location: CSE 305
Can we program computers in our native tongue? This idea, termed natural language programming (NLPRO), has attracted attention almost since the inception of computers themselves. From the point of view of software engineering (SE), efforts to program in natural language (NL) have relied thus far on controlled natural languages (CNL) -– small unambiguous fragments of English with restricted grammars and limited expressivity. Is it possible to replace these CNLs with truly natural, human language? From the point of view of natural language processing (NLP), current technology successfully extracts static information from NL texts. However, the level of NL understanding required for programming in NL goes far beyond such extraction -– it requires human-like interpretation of dynamic processes which are affected by the environment, update states and lead to action. Is it possible to endow computers with this kind of NL understanding? These two questions are fundamental to SE and NLP, respectively. In this talk I argue that the solutions to these seemingly separate challenges are actually closely intertwined, and that one community’s challenge is the other community’s stepping stone for a huge leap and vice versa. Specifically, in this talk I propose to view executable programs in SE as semantic structures in NLP, as the basis for broad-coverage semantic parsing. I present a feasibility study on the statistical modeling of semantic parsing of requirement documents into executable scenarios, where the input documents are written in a restricted yet highly ambiguous fragment of English, and the target representation employs live sequence charts (LSC), a multi-modal visual-executable language for scenario-based programming. The parsing architecture I propose jointly models sentence-level and discourse-level processing in a generative probabilistic framework. I empirically show that the discourse-based model consistently outperforms the sentence-based model when constructing a system that reflects the static (entities, properties) and dynamic (behavioral scenarios) requirements in the document. I conjecture that LSCs, joint sentence-discourse modeling, and statistical learning are key ingredients for effectively tackling the NLPRO long standing challenge, and discuss ways in which NLPRO bots have the potential to change the ways humans and computers interact. BIO: Reut Tsarfaty is a senior lecturer at the department of Mathematics and Computer Science at the Open University in Israel. Reut holds a BSc. from the Technion in Israel and an MSc./PhD. from the Institute for Logic, Language and Computation (ILLC) at the University of Amsterdam. She also held postdoctoral research fellowships at Uppsala University in Sweden and at the Weizmann Institute of Science in Israel. Reut is a recipient of an ERC staring grant from the EU research council, an ISF individual research grant from the Israel science foundation, and a MOSAIC grant from the Dutch Science Foundation (NWO). Reut is a renown expert on statistical parsing of morphologically rich languages (PMRL), she served as a guest editor on PMRL for the Computational Linguistics Journal, and she is the author of the PMRL book (to be published by Morgan and Claypool publishers). Reut's research focuses on statistical models for morphological, syntactic and semantic parsing, and their applications, including (but not limited to) natural language programming, automated essay scoring, and natural language generation.

Thursday, November 10, 2016 - 15:30

Speaker: Mirella Lapata (University of Edinburgh)
Location: EEB 105
Movie analysis is an umbrella term for many tasks aiming to automatically interprete, extract, and summarize the content of a movie. Potential applications include generating shorter versions of scripts to help with the decision making process in a production company, enhancing movie recommendation engines by abstracting over specific keywords to more general concepts (e.g., thrillers with psychopaths), and notably generating movie previews. In this talk I will illustrate how NLP-based models together with video analysis can be used to facilitate various steps in the movie production pipeline. I will formalize the process of generating a shorter version of a movie as the task of finding an optimal chain of scenes and present a graph-based model that selects a chain by jointly optimizing its logical progression, diversity, and importance. I will then apply this framework to screenplay summarization, a task which could enhance script browsing and speed up reading time. I will also show that by aligning the screenplay to the movie, the model can generate movie previews with minimal modification. Finally, I will discuss how the computational analysis of movies can lead to tools that automatically create movie "profiles" which give a first impression of the movie by describing its plot, mood, location, or style. Mirella Lapata is a Professor at the School of Informatics at the University of Edinburgh. Her recent research interests are in natural language processing. She serves as an associate editor of the Journal of Artificial Intelligence Research (JAIR). She is the first recipient (2009) of the British Computer Society and Information Retrieval Specialist Group (BCS/IRSG) Karen Sparck Jones award. She has also received best paper awards in leading NLP conferences and financial support from the EPSRC (the UK Engineering and Physical Sciences Research Council) and ERC (the European Research Council).