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.

Interested in attending CLunch? Sign up for our mailing list here.


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

Christopher Lynn

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.

Dan Goldwasser

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.

Robert Shaffer

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.

Reno Kriz

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.

Daphne Ippolito

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.