CLunch
CLunch is the weekly computational linguistics lunch run by the NLP group. We invite external and internal speakers to come and present their research on natural language processing, computational linguistics, and machine learning.
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Talks
Cornell University
December 3, 2019
Robot Control and Collaboration in Situated Instruction Following
I will present two projects studying the problem of learning to follow natural language instructions. I will present new datasets, a class of interpretable models for instruction following, learning methods that combine the benefits of supervised and reinforcement learning, and new evaluation protocols. In the first part, I will discuss the task of executing natural language instructions with a robotic agent. In contrast to existing work, we do not engineer formal representations of language meaning or the robot environment. Instead, we learn to directly map raw observations and language to low-level continuous control of a quadcopter drone. In the second part, I will propose the task of learning to follow sequences of instructions in a collaborative scenario, where both the user and the system execute actions in the environment and the user controls the system using natural language. To study this problem, we build CerealBar, a multi-player 3D game where a leader instructs a follower, and both act in the environment together to accomplish complex goals. The two projects were led by Valts Blukis, Alane Suhr, and collaborators.
University of Pennsylvania
November 19, 2019
Distributed Semantic Representations from Question-Answering Signals
Human annotations, especially those from experts, are costly for many natural language processing (NLP) tasks. One emerging approach is to use natural language to annotate natural language, but it is challenging to get supervision effectively from annotations that are very different from the target task. This paper studies the case where the annotations are in the format of question answering (QA). We propose a novel approach to retrieve two types of semantic representations from QA, using which we can consistently improve on a suite of tasks. This work may have pointed out an alternative way to supervise NLP tasks.
University of California, San Diego
November 12, 2019
Revisiting post-processing for word embeddings
Word embeddings learnt from large corpora have been adopted in various applications in natural language processing and served as the general input representations to learning systems. Recently, a series of post-processing methods have been proposed to boost the performance of word embeddings on similarity comparison and analogy retrieval tasks, and some have been adapted to compose sentence representations. The general hypothesis behind these methods is that by enforcing the embedding space to be more isotropic, the similarity between words can be better expressed. We view these methods as an approach to shrink the covariance/gram matrix, which is estimated by learning word vectors, towards a scaled identity matrix. By optimising an objective in the semi-Riemannian manifold with Centralised Kernel Alignment (CKA), we are able to search for the optimal shrinkage parameter, and provide a post-processing method to smooth the spectrum of learnt word vectors which yields improved performance on downstream tasks.
University of Pennsylvania
October 29, 2019
A General-Purpose Algorithm for Constrained Sequential Inference
Inference in structured prediction involves finding the best output structure for an input, subject to certain constraints. Many current approaches use sequential inference, which constructs the output in a left-to-right manner. However, there is no general framework to specify constraints in these approaches. We present a principled approach for incorporating constraints into sequential inference algorithms. Our approach expresses constraints using an automaton, which is traversed in lock-step during inference, guiding the search to valid outputs. We show that automata can express commonly used constraints and are easily incorporated into sequential inference. When it is more natural to represent constraints as a set of automata, our algorithm uses an active set method for demonstrably fast and efficient inference. We experimentally show the benefits of our algorithm on constituency parsing and semantic role labeling. For parsing, unlike unconstrained approaches, our algorithm always generates valid output, incurring only a small drop in performance. For semantic role labeling, imposing constraints using our algorithm corrects common errors, improving F1 by 1.5 points. These benefits increase in low-resource settings. Our active set method achieves a 5.2x relative speed-up over a naive approach.
University of Pennsylvania
October 29, 2019
Summary Cloze: A New Task for Content Selection in Topic-Focused Summarization
A key challenge in topic-focused summarization is determining what information should be included in the summary, a problem known as content selection. In this work, we propose a new method for studying content selection in topic-focused summarization called the summary cloze task. The goal of the summary cloze task is to generate the next sentence of a summary conditioned on the beginning of the summary, a topic, and a reference document(s). The main challenge is deciding what information in the references is relevant to the topic and partial summary and should be included in the summary. Although the cloze task does not address all aspects of the traditional summarization problem, the more narrow scope of the task allows us to collect a large-scale datset of nearly 500k summary cloze instances from Wikipedia. We report experimental results on this new dataset using various extractive models and a two-step abstractive model that first extractively selects a small number of sentences and then abstractively summarizes them. Our results show that the topic and partial summary help the models identify relevant content, but the task remains a significant challenge.
University of Pennsylvania
October 29, 2019
"Going on a vacation" takes longer than "Going for a walk": A Study of Temporal Commonsense Understanding
Understanding time is crucial for understanding events expressed in natural language. Because people rarely say the obvious, it is often necessary to have commonsense knowledge about various temporal aspects of events, such as duration, frequency, and temporal order. However, this important problem has so far received limited attention. This paper systematically studies this temporal commonsense problem. Specifically, we define five classes of temporal commonsense, and use crowdsourcing to develop a new dataset, MCTACO, that serves as a test set for this task. We find that the best current methods used on MCTACO are still far behind human performance, by about 20%, and discuss several directions for improvement. We hope that the new dataset and our study here can foster more future research on this topic.
New York University
October 22, 2019
Neural Networks for Morphological Generation in the Minimal-Resource Setting
As languages other than English are moving more and more into the focus of natural language processing, accurate handling of morphology is increasing in importance. This talk presents neural network-based approaches to morphological generation, casting the problem as a character-based sequence-to-sequence task. First, we will generally discuss how to successfully train neural sequence-to-sequence models for this. Then, since many morphologically rich languages only have limited resources, the main part of the talk will focus on how to overcome the challenges that limited amounts of annotated training data pose to neural models. The approaches covered in this talk include multi-task learning, cross-lingual transfer learning, and meta-learning.
Jithin Pradeep
The Vanguard Group
October 15, 2019
ArSI - Artificial Speech Intelligence - An end to end automatic speech recognition using Attention plus CTC
Shi Yu
The Vanguard Group
October 15, 2019
A Financial Service Chatbot based on Deep Bidirectional Transformers
University of Pennsylvania
October 8, 2019
Human information processing in complex networks
Humans communicate using systems of interconnected stimuli or concepts -- from language and music to literature and science -- yet it remains unclear how, if at all, the structure of these networks supports the communication of information. Although information theory provides tools to quantify the information produced by a system, traditional metrics do not account for the inefficient and biased ways that humans process this information. Here we develop an analytical framework to study the information generated by a system as perceived by a human observer. We demonstrate experimentally that this perceived information depends critically on a system's network topology. Applying our framework to several real networks, we find that they communicate a large amount of information (having high entropy) and do so efficiently (maintaining low divergence from human expectations). Moreover, we show that such efficient communication arises in networks that are simultaneously heterogeneous, with high-degree hubs, and clustered, with tightly-connected modules -- the two defining features of hierarchical organization. Together, these results suggest that many real networks are constrained by the pressures of information transmission, and that these pressures select for specific structural features.
Purdue University
October 1, 2019
Joint Models for Social, Behavioral and Textual Information
Understanding natural language communication often requires context, such as the speakers' backgrounds and social conventions, however, when it comes to computationally modeling these interactions, we typically ignore their broader context and analyze the text in isolation. In this talk, I will review on-going work demonstrating the importance of holistically modeling behavioral, social and textual information. I will focus on several NLP problems, including political discourse analysis on Twitter, partisan news detection and open-domain debate stance prediction, and discuss how jointly modeling text and social behavior can help reduce the supervision effort and provide a better representation for language understanding tasks.
University of Pennsylvania
September 24, 2019
Similarity Inference for Legal Texts
Quantifying similarity between pairs of documents is a ubiquitous task. Both researchers and members of the public frequently use document-level pairwise similarity measures to describe or explore unfamiliar corpora, or to test hypotheses regarding diffusion of ideas between authors. High-level similarity measures are particularly useful when dealing with legal or political corpora, which often contain long, thematically diverse, and specialized language that is difficult for non-experts to interpret. Unfortunately, though similarity estimation is a well-studied problem in the context of short documents and document excerpts, less attention has been paid to the problem of similarity inference for long documents.
University of Pennsylvania
September 17, 2019
Comparison of Diverse Decoding Methods from Conditional Language Models
While conditional language models have greatly improved in their ability to output high-quality natural language, many NLP applications benefit from being able to generate a diverse set of candidate sequences. Diverse decoding strategies aim to, within a given-sized candidate list, cover as much of the space of high-quality outputs as possible, leading to improvements for tasks that re-rank and combine candidate outputs. Standard decoding methods, such as beam search, optimize for generating high likelihood sequences rather than diverse ones, though recent work has focused on increasing diversity in these methods. We conduct an extensive survey of decoding-time strategies for generating diverse outputs from conditional language models. We also show how diversity can be improved without sacrificing quality by over-sampling additional candidates, then filtering to the desired number.
University of Pennsylvania
September 17, 2019
Detecting whether Text is Human- or Machine-Generated
With the advent of generative models with a billion parameters or more, it is now possible to automatically generate vast amounts of human-sounding text. But just how human-like is this machine-generated text? Intuitively, shorter amounts of machine-generated text are harder to detect, but exactly how many words can a machine generate and still fool both humans and trained discriminators? We investigate how the choices of sampling strategy and text sequence length impact discriminability from human-written text, using both automatic detection methods and human judgement.