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  • Learning Challenges in Natural Language Processing Thumbnail

    Learning Challenges in Natural Language Processing

    April 8, 2019  |  Swabha Swayamdipta
    As the availability of data for language learning grows, the role of linguistic structure is under scrutiny. At the same time, it is imperative to closely inspect patterns in data which might present loopholes for models to obtain high performance on benchmarks. In a two-part talk, I will address each of these challenges. First, I will introduce the paradigm of scaffolded learning. Scaffolds enable us to leverage inductive biases from one structural source for prediction of a different, but related structure, using only as much supervision as is necessary. We show that the resulting representations achieve improved performance across a range of tasks, indicating that linguistic structure remains beneficial even with powerful deep learning architectures. In the second part of the talk, I will showcase some of the properties exhibited by NLP models in large data regimes. Even as these models report excellent performance, sometimes claimed to beat humans, a closer look reveals that predictions are not a result of complex reasoning, and the task is not being completed in a generalizable way. Instead, this success can be largely attributed to exploitation of some artifacts of annotation in the datasets. I will discuss some questions our finding raises, as well as directions for future work.
  • Learning Structured Information from Language Thumbnail

    Learning Structured Information from Language

    April 3, 2019  |  Arzoo Katiyar
    Extracting information from text entails deriving a structured, and typically domain-specific, representation of entities and relations from unstructured text. The information thus extracted can potentially facilitate applications such as question answering, information retrieval, conversational dialogue and opinion analysis. However, extracting information from text in a structured form is difficult: it requires understanding words and the relations that exist between them in the context of both the current sentence and the document as a whole. In this talk, I will present my research on neural models that learn structured output representations comprised of textual mentions of entities and relations within a sentence. In particular, I will propose the use of novel output representations that allow the neural models to learn better dependencies in the output structure and achieve state-of-the-art performance on both tasks as well as on nested variations. I will also describe our recent work on expanding the input context beyond sentences by incorporating coreference resolution to learn entity-level rather than mention-level representations and show that these representations can capture the information regarding the saliency of entities in the document.
  • Natural Language Understanding with Indirect Supervision Thumbnail

    Natural Language Understanding with Indirect Supervision

    March 29, 2019  |  Daniel Khashabi
    Can we solve language understanding tasks without relying on task-specific annotated data? This could be important in scenarios where the inputs range across various domains and it is expensive to create annotated data. I discuss two different language understanding problems (Question Answering and Entity Typing) which have traditionally relied on on direct supervision. For these problems, I present two recent works where exploiting properties of the underlying representations and indirect signals help us move beyond traditional paradigms. And as a result, we observe better generalization across domains.
  • Spatiotemporal understanding of people using scenes, objects and poses Thumbnail

    Spatiotemporal understanding of people using scenes, objects and poses

    March 11, 2019  |  Rohit Girdhar
    Humans are arguably one of the most important entities that AI systems would need to understand to be useful and ubiquitous. From autonomous cars observing pedestrians, to assistive robots helping the elderly, a large part of this understanding is focused on recognizing human actions, and potentially, their intentions. Humans themselves are quite good at this task: we can look at a person and explain in great detail every action they are doing. Moreover, we can reason over those actions over time, and even predict what potential actions they may intend do in the future. Computer vision algorithms, on the other hand, have lagged far behind on this task. In my research, I’ve explored techniques to improve human action understanding from a visual input, with the key insight being that human actions are dependent on the state of their environment (parameterized by the scene and the objects in it) apart from their own state (parameterized by their pose). In this talk, I will talk about three key ways I exploit this dependence: (1) Learning to aggregate this contextual information to recognize human actions; (2) Predicting a prior on human actions by learning about the affordances of the scenes and objects they interact with; and finally, (3) Moving towards longer term temporal reasoning through a new dataset and benchmark tasks.
  • AI & Policy Workshop Thumbnail

    AI & Policy Workshop

    March 7, 2019  |  
    "An Ethical Crisis in Computing?" Moshe Vardi | Karen Ostrum George Distinguished Professor, Computational Engineering, Rice University "Algorithmic Accountability: Designing for Safety" Ben Shneiderman | Distinguished Professor, Department of Computer Science, University of Maryland, College Park "AI Policy: What to Do Now, Soon, and One Day" Ryan Calo | Lane Powell & D. Wayne Gittinger Associate Professor of Law, University of Washington "Less Talk, More Do: Applied Ethics in AI" Tracy Kosa | Adjunct Professor, Faculty of Law and Albers School of Business, Seattle University Panel Q&A Oren Etzioni and speakers
  • Natural Language Programming (NLPRO): Turning Texts into Executable Code Thumbnail

    Natural Language Programming (NLPRO): Turning Texts into Executable Code

    March 1, 2019  |  Reut Tsarfaty
    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 information from NL texts. However, the level of NL understanding required for programming in NL goes far beyond such information extraction. Is it possible to endow computers with a dynamic kind of NL understanding? 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 semantic parsing of requirements documents into executable scenarios, where the requirements are written in a restricted yet highly ambiguous fragment of English, and the target representation employs live sequence charts (LSC), a multi-modal executable programming language. 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, constructing a system that reflects both the static (entities, properties) and dynamic (behavioral scenarios) requirements in the input document.
  • Where’s the Data: A new approach to social science data search and discovery Thumbnail

    Where’s the Data: A new approach to social science data search and discovery

    February 5, 2019  |  Julia Lane
    The social sciences are at a crossroads The great challenges of our time are human in nature - terrorism, climate change, the use of natural resources, and the nature of work - and require robust social science to understand the sources and consequences. Yet the lack of reproducibility and replicability evident in many fields is even more acute in the study of human behavior both because of the difficulty of sharing confidential data and because of the lack of scientific infrastructure. Much of the core infrastructure is manual and ad-hoc in nature, threatening the legitimacy and utility of social science research. A major challenge is search and discovery. The vast majority of social science data and outputs cannot be easily discovered by other researchers even when nominally deposited in the public domain. A new generation of automated search tools could help researchers discover how data are being used, in what research fields, with what methods, with what code and with what findings. And automation can be used to reward researchers who validate the results and contribute additional information about use, fields, methods, code, and findings. In sum, the use of data depends critically on knowing how it has been produced and used before: the required elements what do the data measure, what research has been done by what researchers, with what code, and with what results. In this presentation I describe the work that we are doing to build and develop automated tools to create the equivalent of an or TripAdvisor for the access and use of confidential microdata.
  • Understanding Time In Natural Language Thumbnail

    Understanding Time In Natural Language

    January 25, 2019  |  Qiang Ning
    Time is an important dimension when we describe the world because the world is evolving over time and many facts are time-sensitive. Understanding time is thus an important aspect of natural language understanding and many applications may rely on it, e.g., information retrieval, summarization, causality, and question answering. In this talk, I will mainly focus on a key component of it, temporal relation extraction. The task has long been challenging because the actual timestamps of those events are rarely expressed explicitly and their temporal order has to be inferred, from lexical cues, between the lines, and often based on strong background knowledge. Additionally, collecting enough and high-quality annotations to facilitate machine learning algorithms for this task is also difficult, which makes it even more challenging to investigate the task. I tackled this task in three perspectives, structured learning, common sense, and data collection, and have improved the state-of-the-art by approximately 20% in absolute F1. My current system, CogCompTime, is available at this online demo: In the future, I expect to expand my research in these directions to other core problems in AI such as incidental supervision, semantic parsing, and knowledge representation.
  • Text Generation from Knowledge Graphs Thumbnail

    Text Generation from Knowledge Graphs

    January 11, 2019  |  Rik Koncel-Kedziorski
    In this talk I will introduce a new model for encoding knowledge graphs and generating texts from them. Graphical knowledge representations are ubiquitous in computing, but pose a challenge for text generation techniques due to their non-hierarchical structure and collapsing of long-distance dependencies. Moreover, automatically extracted knowledge is noisy, and so requires a text generation model be robust. To address these issues, I introduce a novel attention-based encoder-decoder model for knowledge-graph-to-text generation. This model extends the popular Transformer for text encoding to function over graph-structured inputs. The result is a powerful, general model for graph encoding which can incorporate global structural information when contextualizing vertices in their local neighborhoods. Through detailed automatic and human evaluations I demonstrate the value of conditioning text generation on graph-structured knowledge, as well as the superior performance of the proposed model compared to recent work.
  • Using cognitive science to evaluate and interpret neural language models Thumbnail

    Using cognitive science to evaluate and interpret neural language models

    December 14, 2018  |  Tal Linzen
    Recent technological advances have made it possible to train recurrent neural networks (RNNs) on a much larger scale than before. While these networks have proved effective in NLP applications, their limitations and the mechanisms by which they accomplish their goals are poorly understood. In this talk, I will show how methods from cognitive science can help elucidate and improve the syntactic representations employed by RNN language models. I will review evidence that RNN language models are able to process syntactic dependencies in typical sentences with considerable success across languages (Linzen et al 2016, TACL; Gulordava et al. 2018, NAACL). However, when evaluated on experimentally controlled materials, their error rate increases sharply; explicit syntactic supervision mitigates the drop in performance (Marvin & Linzen 2018, EMNLP). Finally, I will discuss how language model adaptation can provide a tool for probing RNN syntactic representations, following the inspiration of the syntactic priming paradigm from psycholinguistics (van Schijndel & Linzen 2018, EMNLP).