Welcome back everyone to the Fall 2016 semester! This fall we will have a new time for the NLP Seminar; we’ll be meeting on Mondays from 3:30-4:30pm. The location is the same, room 205 South Hall.
Another exciting change is that UC Berkeley students can earn 1 unit of credit for attending the meeting on a weekly basis. Grad students can sign up under I School 290 or CS 294.
Breaking news: undergraduates from any department can now sign up for INFO 190-001 (CCN 34812)
We’ll continue with invited speakers for alternating weeks, and in the intervening weeks we’ll discuss research papers and related activities. We’ll continue to post speaker information on this web site.
Please join us for the NLP Seminar this Thursday (April 21) at 4pm in 205 South Hall. All are welcome!
Speaker: Dan Gillick (Google)
Title: Multilingual language processing from bytes
I’ll describe my recent work on standard language processing tasks like part-of-speech tagging and named entity recognition where I replace the traditional pipeline of models with a recurrent neural network. In particular, the model reads one byte at a time (it doesn’t know anything about tokens or sentences) and produces output over byte spans. This allows for very compact, multilingual models that improve over models trained on a single language. I’ll show lots of results and we can discuss the merits and problems with this approach.
Please join us for the next NLP Seminar Thursday, April 7 at 4pm in 205 South Hall. All are welcome!
Speaker: Percy Liang (Stanford)
Title: Learning from Zero
Can we learn if we start with zero examples, either labeled or unlabeled? This scenario arises in new user-facing systems (such as virtual assistants for new domains), where inputs should come from users, but no users exist until we have a working system, which depends on having training data. I discuss recent work that circumvent this circular dependence by interleaving user interaction and learning.
Please join us for the next NLP Seminar Thursday, March 10 at 4pm in 205 South Hall. All are welcome!
Speaker: Justine Kao (Stanford)
Title: Computational approaches to creative and social language
Humans are highly creative users of language. From poetry, to puns, to political satire, much of human communication requires listeners to go beyond the literal meanings of words to infer an utterance’s subtext as well as the speaker’s intentions. Here I will present research on four types of creative language: hyperbole, irony, wordplay, and poetry. In the first part of the talk, I will describe an extension to Rational Speech Act (RSA) models, a family of computational models that view language understanding as recursive social reasoning. Using a series of behavioral experiments, I show that the RSA model predicts people’s interpretations of hyperbole and irony with high accuracy. In the second part of the talk, I will describe formal measures of humor and poetic beauty that incorporate insights from psychology and literary theory to produce quantitative predictions. Finally, I will discuss ways in which cognitive modeling and NLP can provide complementary approaches to understanding the social aspects of language use.
Please join us for the next NLP Seminar on Thursday February 25 at 4pm in 205 South Hall. All are welcome!
Speaker: David Mimno (Cornell)
Title: Topic models without the randomness: new perspectives on deterministic algorithms
Topic models provide a useful way to identify and measure constructs in large text collections, such as themes, genres, discourses, and topics. But running popular algorithms multiple times on the same documents can produce different results, raising questions about the reliability of any resulting conclusions. I will summarize an exciting new line of research in deterministic algorithms for topic inference that trade stronger model assumptions for provably optimal performance. This new approach not only leads to better models but better computational scalability and a richer understanding of connections between topic models and related methods like LSI and word embeddings.
Please join us for the next NLP Seminar on Thursday Feb 11 at 4pm in 205 South Hall. All are welcome!
Speaker: Angel Chang (Stanford)
Title: Interactive text to 3D scene generation
Designing 3D scenes is currently a creative task that requires significant expertise and effort in using complex 3D design interfaces. This design process starts in contrast to the easiness with which people can use language to describe real and imaginary environments. We present an interactive text to 3D scene generation system that allows a user to design 3D scenes using natural language. A user provides input text from which we extract explicit constraints on the objects that should appear in the scene. Given these explicit constraints, the system then uses a spatial knowledge base learned from an existing database of 3D scenes and 3D object models to infer an arrangement of the objects forming a natural scene matching the input description. Using textual commands the user can then iteratively refine the created scene by adding, removing, replacing, and manipulating objects.
Please join us for the next NLP Seminar on Thursday January 28 at 4pm in 205 South Hall. All are welcome!
Speaker: Greg Durrett (UC Berkeley)
Title: Harnessing Big Data for Text Analysis with Joint Models
One reason that analyzing text is hard is that it involves dealing with deeply entangled linguistic variables: objects like syntactic structures, semantic types, and discourse relations depend on one another in complex ways. Our work uses joint modeling approaches to tackle several facets of text analysis, combining model components both across and within subtasks. This model structure allows us to pass information between these entangled subtasks and propagate high-confidence predictions rather than errors. Critically, our models have the capacity to learn key linguistic phenomena as well as other important patterns in the data; that is, linguistics tells us how to structure these models, then the data injects knowledge into them. We describe state-of-the-art systems for a range of tasks, including syntactic parsing, entity analysis, and document summarization.
We’ll be meeting Spring semester at the same time and same location: alternating Thursdays at 4pm, 205 South Hall.
Our schedule is:
Greg Durrett (Jan 28)
Angel Chang (Feb 11)
David Mimno (Feb 25)
Justine Kao (Mar 10)
Spring Break (Mar 24)
Percy Liang (Apr 7)
Dan Gillick (Apr 21)
Please join us for the next NLP Seminar this Thursday (Dec. 3) at 4pm in 205 South Hall.
Speaker: Vinodkumar Prabhakaran, Stanford (www.cs.stanford.edu/~vinod)
Title: Social Power in Interactions: Computational Analysis and Detection of Power
Abstract: In this talk, I will present study done as part of my thesis research on how social power relations affect the way people interact with one another and how we can use statistical machine learning techniques to detect these power relations automatically. This study is performed in the domain of organizational emails using the Enron email corpus. I will first present the problem of predicting superior-subordinate relationship between pairs of people, based solely on the language and structure of interactions within single email threads. We found many dialog behavior patterns that are salient to the direction of power. For example, superiors tend to send shorter messages, and use more overt displays of power than subordinates. I will then present the results of our investigation on how the gender of the participants impacts the manifestations of power. For example, do male superiors and female superiors differ in how often they use overt displays of power?
For this week’s meeting, rather than focusing on completed work, those
interested are invited to request feedback on current and future research efforts.
Those working on something now who could benefit from feedback from the seminar, or who have a half-baked idea on some new direction that you want feedback on shaping, plan to talk for about 10 minutes each. Graduate students and undergraduate seminar participants are welcome to participate.