Using end-to-end models for speech translation (ST) has increasingly been the focus of the ST community. These models condense the previously cascaded systems by directly converting sound waves into translated text. However, cascaded models have the advantage of including automatic speech recognition output, useful for a variety of practical ST systems that often display transcripts to the user alongside the translations. To bridge this gap, recent work has shown initial progress into the feasibility for end-to-end models to produce both of these outputs. However, all previous work has only looked at this problem from the consecutive perspective, leaving uncertainty on whether these approaches are effective in the more challenging streaming setting. We develop an end-to-end streaming ST model based on a re-translation approach and compare against standard cascading approaches. We also introduce a novel inference method for the joint case, interleaving both transcript and translation in generation and removing the need to use separate decoders. Our evaluation across a range of metrics capturing accuracy, latency, and consistency shows that our end-to-end models are statistically similar to cascading models, while having half the number of parameters. We also find that both systems provide strong translation quality at low latency, keeping 99% of consecutive quality at a lag of just under a second.

Text classification is a significant branch of natural language processing, and has many applications including document classification and sentiment analysis. Unsurprisingly, those who do text classification are concerned with the run-time of their algorithms, many of which depend on the size of the corpus’ vocabulary due to their bag-of-words representation. Although many studies have examined the effect of preprocessing techniques on vocabulary size and accuracy, none have examined how these methods affect a model’s run-time. To fill this gap, we provide a comprehensive study that examines how preprocessing techniques affect the vocabulary size, model performance, and model run-time, evaluating ten techniques over four models and two datasets. We show that some individual methods can reduce run-time with no loss of accuracy, while some combinations of methods can trade 2-5% of the accuracy for up to a 65% reduction of run-time. Furthermore, some combinations of preprocessing techniques can even provide a 15% reduction in run-time while simultaneously improving model accuracy.

Typically, machine learning systems solve new tasks by training on thousands of examples. In contrast, humans can solve new tasks by reading some instructions, with perhaps an example or two. To take a step toward closing this gap, we introduce a framework for developing NLP systems that solve new tasks after reading their descriptions, synthesizing prior work in this area. We instantiate this frame- work with a new English language dataset, ZEST, structured for task-oriented evaluation on unseen tasks. Formulating task descriptions as questions, we ensure each is general enough to apply to many possible inputs, thus comprehensively evaluating a model’s ability to solve each task. Moreover, the dataset’s structure tests specific types of systematic generalization. We find that the state-of-the-art T5 model achieves a score of 12% on ZEST, leaving a significant challenge for NLP researchers.

Humor is a complicated language phenomenon that depends upon many factors, including topic, date, and recipient. Because of this variation, it can be hard to determine what exactly makes a joke humorous, leading to difficulties in joke identification and related tasks. Furthermore, current humor datasets are lacking in both joke variety and size, with almost all current datasets having less than 100k jokes. In order to alleviate this issue we compile a collection of over 550,000 jokes posted over an 11 year period on the Reddit r/Jokes subreddit (an online forum), providing a large scale humor dataset that can easily be used for a myriad of tasks. This dataset also provides quantitative metrics for the level of humor in each joke, as determined by subreddit user feedback. We explore this dataset through the years, examining basic statistics, most mentioned entities, and sentiment proportions. We also introduce this dataset as a task for future work, where models learn to predict the level of humor in a joke. On that task we provide strong state-of-the-art baseline models and show room for future improvement. We hope that this dataset will not only help those researching computational humor, but also help social scientists who seek to understand popular culture through humor.

Predicting reading time has been a subject of much previous work, focusing on how different words affect human processing, measured by reading time. However, previous work has dealt with a limited number of participants as well as word level only predictions (i.e. predicting the time to read a single word). We seek to extend these works by examining whether or not document level predictions are effective, given additional information such as subject matter, font characteristics, and readability metrics. We perform a novel experiment to examine how different features of text contribute to the time it takes to read, distributing and collecting data from over a thousand participants. We then employ a large number of machine learning methods to predict a user’s reading time. We find that despite extensive research showing that word level reading time can be most effectively predicted by neural networks, larger scale text can be easily and most accurately predicted by one factor, the number of words.

Understanding and identifying humor has been increasingly popular, as seen by the number of datasets created to study humor. However, one area of humor research, humor generation, has remained a difficult task, with machine generated jokes failing to match human-created humor. As many humor prediction datasets claim to aid in generative tasks, we examine whether these claims are true. We focus our experiments on the most popular dataset, included in the 2020 SemEval’s Task 7, and teach our model to take normal text and “translate” it into humorous text. We evaluate our model compared to humorous human generated headlines, finding that our model is preferred equally in A/B testing with the human edited versions, a strong success for humor generation, and is preferred over an intelligent random baseline 72% of the time. We also show that our model is assumed to be human written comparable with that of the human edited headlines and is significantly better than random, indicating that this dataset does indeed provide potential for future humor generation systems.

Much previous work has been done in attempting to identify humor in text. In this paper we extend that capability by proposing a new task: assessing whether or not a joke is humorous. We present a novel way of approaching this problem by building a model that learns to identify humorous jokes based on ratings gleaned from Reddit pages, consisting of almost 16,000 labeled instances. Using these ratings to determine the level of humor, we then employ a Transformer architecture for its advantages in learning from sentence context. We demonstrate the effectiveness of this approach and show results that are comparable to human performance. We further demonstrate our model’s increased capabilities on humor identification problems, such as the previously created datasets for short jokes and puns. These experiments show that this method outperforms all previous work done on these tasks, with an F-measure of 93.1% for the Puns dataset and 98.6% on the Short Jokes dataset.