D3.8 Lexical-semantic analytics for NLP

UIDB/03213/2020 UIDP/03213/2020The present document illustrates the work carried out in task 3.3 (work package 3) of ELEXIS project focused on lexical-semantic analytics for Natural Language Processing (NLP). This task aims at computing analytics for lexical-semantic information such as words, senses and domains in the available resources, investigating their role in NLP applications. Specifically, this task concentrates on three research directions, namely i) sense clustering, in which grouping senses based on their semantic similarity improves the performance of NLP tasks such as Word Sense Disambiguation (WSD), ii) domain labeling of text, in which the lexicographic resources made available by the ELEXIS project for research purposes allow better performances to be achieved, and finally iii) analysing the diachronic distribution of senses, for which a software package is made available.publishersversionpublishe

SRL4E - Semantic Role Labeling for Emotions: A Unified Evaluation Framework

In the field of sentiment analysis, several studies have highlighted that a single sentence may express multiple, sometimes contrasting, sentiments and emotions, each with its own experiencer, target and/or cause. To this end, over the past few years researchers have started to collect and annotate data manually, in order to investigate the capabilities of automatic systems not only to distinguish between emotions, but also to capture their semantic constituents. However, currently available gold datasets are heterogeneous in size, domain, format, splits, emotion categories and role labels, making comparisons across different works difficult and hampering progress in the area. In this paper, we tackle this issue and present a unified evaluation framework focused on Semantic Role Labeling for Emotions (SRL4E), in which we unify several datasets tagged with emotions and semantic roles by using a common labeling scheme. We use SRL4E as a benchmark to evaluate how modern pretrained language models perform and analyze where we currently stand in this task, hoping to provide the tools to facilitate studies in this complex area

CycleDRUMS: automatic drum arrangement for bass lines using CycleGAN

Abstract The two main research threads in computer-based music generation are the construction of autonomous music-making systems and the design of computer-based environments to assist musicians. In the symbolic domain, the key problem of automatically arranging a piece of music was extensively studied, while relatively fewer systems tackled this challenge in the audio domain. In this contribution, we propose CycleDRUMS, a novel method for generating drums given a bass line. After converting the waveform of the bass into a mel-spectrogram, we can automatically generate original drums that follow the beat, sound credible, and be directly mixed with the input bass. We formulated this task as an unpaired image-to-image translation problem, and we addressed it with CycleGAN, a well-established unsupervised style transfer framework designed initially for treating images. The choice to deploy raw audio and mel-spectrograms enabled us to represent better how humans perceive music and to draw sounds for new arrangements from the vast collection of music recordings accumulated in the last century. In the absence of an objective way of evaluating the output of both generative adversarial networks and generative music systems, we further defined a possible metric for the proposed task, partially based on human (and expert) judgment. Finally, as a comparison, we replicated our results with Pix2Pix, a paired image-to-image translation network, and we showed that our approach outperforms it