Drawing Causal Inferences About Performance Effects in NLP

This article emphasizes that NLP as a science seeks to make inferences about the performance effects that result from applying one method (compared to another method) in the processing of natural language. Yet NLP research in practice usually does not achieve this goal: In NLP research articles, typically only a few models are compared. Each model results from a specific procedural pipeline (here named processing system) that is composed of a specific collection of methods that are used in preprocessing, pretraining, hyperparameter tuning, and training on the target task. To make generalizing inferences about the performance effect that is caused by applying some method A vs. another method B, it is not sufficient to compare a few specific models that are produced by a few specific (probably incomparable) processing systems. Rather, the following procedure would allow drawing inferences about methods' performance effects: (1) A population of processing systems that researchers seek to infer to has to be defined. (2) A random sample of processing systems from this population is drawn. (The drawn processing systems in the sample will vary with regard to the methods they apply along their procedural pipelines and also will vary regarding the compositions of their training and test data sets used for training and evaluation.) (3) Each processing system is applied once with method A and once with method B. (4) Based on the sample of applied processing systems, the expected generalization errors of method A and method B are approximated. (5) The difference between the expected generalization errors of method A and method B is the estimated average treatment effect due to applying method A compared to method B in the population of processing systems.

A Comparison of Approaches for Imbalanced Classification Problems in the Context of Retrieving Relevant Documents for an Analysis

One of the first steps in many text-based social science studies is to retrieve documents that are relevant for the analysis from large corpora of otherwise irrelevant documents. The conventional approach in social science to address this retrieval task is to apply a set of keywords and to consider those documents to be relevant that contain at least one of the keywords. But the application of incomplete keyword lists risks drawing biased inferences. More complex and costly methods such as query expansion techniques, topic model-based classification rules, and active as well as passive supervised learning could have the potential to more accurately separate relevant from irrelevant documents and thereby reduce the potential size of bias. Yet, whether applying these more expensive approaches increases retrieval performance compared to keyword lists at all, and if so, by how much, is unclear as a comparison of these approaches is lacking. This study closes this gap by comparing these methods across three retrieval tasks associated with a data set of German tweets (Linder, 2017), the Social Bias Inference Corpus (SBIC) (Sap et al., 2020), and the Reuters-21578 corpus (Lewis, 1997). Results show that query expansion techniques and topic model-based classification rules in most studied settings tend to decrease rather than increase retrieval performance. Active supervised learning, however, if applied on a not too small set of labeled training instances (e.g. 1,000 documents), reaches a substantially higher retrieval performance than keyword lists.

Neural Transfer Learning with Transformers for Social Science Text Analysis

During the last years, there have been substantial increases in the prediction performances of natural language processing models on text-based supervised learning tasks. Especially deep learning models that are based on the Transformer architecture (Vaswani et al., 2017) and are used in a transfer learning setting have contributed to this development. As Transformer-based models for transfer learning have the potential to achieve higher prediction accuracies with relatively few training data instances, they are likely to benefit social scientists that seek to have as accurate as possible text-based measures but only have limited resources for annotating training data. To enable social scientists to leverage these potential benefits for their research, this paper explains how these methods work, why they might be advantageous, and what their limitations are. Additionally, three Transformer-based models for transfer learning, BERT (Devlin et al., 2019), RoBERTa (Liu et al., 2019), and the Longformer (Beltagy et al., 2020), are compared to conventional machine learning algorithms on three social science applications. Across all evaluated tasks, textual styles, and training data set sizes, the conventional models are consistently outperformed by transfer learning with Transformer-based models, thereby demonstrating the potential benefits these models can bring to text-based social science research.