diagNNose: A Library for Neural Activation Analysis

In this paper we introduce diagNNose, an open source library for analysing the activations of deep neural networks. diagNNose contains a wide array of interpretability techniques that provide fundamental insights into the inner workings of neural networks. We demonstrate the functionality of diagNNose with a case study on subject-verb agreement within language models. diagNNose is available at https://github.com/i-machine-think/diagnnose.Comment: Accepted to the Third BlackboxNLP Workshop on Analyzing and Interpreting Neural Networks for NLP, EMNLP 202

Language Modelling as a Multi-Task Problem

In this paper, we propose to study language modelling as a multi-task problem, bringing together three strands of research: multi-task learning, linguistics, and interpretability. Based on hypotheses derived from linguistic theory, we investigate whether language models adhere to learning principles of multi-task learning during training. To showcase the idea, we analyse the generalisation behaviour of language models as they learn the linguistic concept of Negative Polarity Items (NPIs). Our experiments demonstrate that a multi-task setting naturally emerges within the objective of the more general task of language modelling.We argue that this insight is valuable for multi-task learning, linguistics and interpretability research and can lead to exciting new findings in all three domains.Comment: Accepted for publication at EACL 202

Language models use monotonicity to assess NPI licensing

We investigate the semantic knowledge of language models (LMs), focusing on (1) whether these LMs create categories of linguistic environments based on their semantic monotonicity properties, and (2) whether these categories play a similar role in LMs as in human language understanding, using negative polarity item licensing as a case study. We introduce a series of experiments consisting of probing with diagnostic classifiers (DCs), linguistic acceptability tasks, as well as a novel DC ranking method that tightly connects the probing results to the inner workings of the LM. By applying our experimental pipeline to LMs trained on various filtered corpora, we are able to gain stronger insights into the semantic generalizations that are acquired by these models.Comment: Published in ACL Findings 202

Overshooting of Clean Tropospheric Air in the Tropical Lower Stratosphere as Seen by the CALIPSO Lidar

The evolution of aerosols in the tropical upper troposphere/lower stratosphere between June 2006 and October 2009 is examined using the observations of the space borne CALIOP lidar aboard the CALIPSO satellite. Superimposed on several volcanic plumes and soot from an extreme biomass-burning event in 2009, the measurements reveal the existence of fast cleansing episodes of the lower stratosphere to altitudes as high as 20 km. The cleansing of the full 14-20km layer takes place within 1-4 months. Its coincidence with the maximum of convective activity in the southern tropics, suggests that the cleansing is the result of a large number of overshooting towers, injecting aerosol-poor tropospheric air into the lower stratosphere. The enhancements of aerosols at the tropopause level during the NH summer may be due to the same transport process but associated with intense sources of aerosols at the surface. Since, the tropospheric air flux derived from CALIOP observations during North Hemisphere winter is 5 20 times larger than the slow ascent by radiative heating usually assumed, the observations suggest that convective overshooting is a major contributor to troposphere-to-stratosphere transport with concommitant implications to the Tropical Tropopause Layer top height, chemistry and thermal structure