Disentangling the Properties of Human Evaluation Methods:A Classification System to Support Comparability, Meta-Evaluation and Reproducibility Testing

Current standards for designing and reporting human evaluations in NLP mean it is generally unclear which evaluations are comparable and can be expected to yield similar results when applied to the same system outputs. This has serious implications for reproducibility testing and meta-evaluation, in particular given that human evaluation is considered the gold standard against which the trustworthiness of automatic metrics is gauged. %and merging others, as well as deciding which evaluations should be able to reproduce each other’s results. Using examples from NLG, we propose a classification system for evaluations based on disentangling (i) what is being evaluated (which aspect of quality), and (ii) how it is evaluated in specific (a) evaluation modes and (b) experimental designs. We show that this approach provides a basis for determining comparability, hence for comparison of evaluations across papers, meta-evaluation experiments, reproducibility testing

Graph Neural Networks for Natural Language Processing: A Survey

Deep learning has become the dominant approach in coping with various tasks in Natural LanguageProcessing (NLP). Although text inputs are typically represented as a sequence of tokens, there isa rich variety of NLP problems that can be best expressed with a graph structure. As a result, thereis a surge of interests in developing new deep learning techniques on graphs for a large numberof NLP tasks. In this survey, we present a comprehensive overview onGraph Neural Networks(GNNs) for Natural Language Processing. We propose a new taxonomy of GNNs for NLP, whichsystematically organizes existing research of GNNs for NLP along three axes: graph construction,graph representation learning, and graph based encoder-decoder models. We further introducea large number of NLP applications that are exploiting the power of GNNs and summarize thecorresponding benchmark datasets, evaluation metrics, and open-source codes. Finally, we discussvarious outstanding challenges for making the full use of GNNs for NLP as well as future researchdirections. To the best of our knowledge, this is the first comprehensive overview of Graph NeuralNetworks for Natural Language Processing.Comment: 127 page

Human evaluation and statistical analyses on machine reading comprehension, question generation and open-domain dialogue

Evaluation is a critical element in the development process of many natural language based systems. In this thesis, we will present critical analyses of standard evaluation methodologies applied in the following Natural Language Processing (NLP) domains: machine reading comprehension (MRC), question generation (QG), and open-domain dialogue. Generally speaking, systems from tasks like MRC are usually evaluated by comparing the similarity between hand-crafted references and system generated outputs using automatic evaluation metrics, thus these metrics are mainly borrowed from other NLP tasks that have been well-developed, such as machine translation and text summarization. Meanwhile, the evaluation of QG and dialogues is even a known open problem as such tasks do not have the corresponding references for computing the similarity, and human evaluation is indispensable when assessing the performance of the systems from these tasks. However, human evaluation is unfortunately not always valid because: i) human evaluation may cost too much and be hard to deploy when experts are involved; ii) human assessors can lack reliability in the crowd-sourcing environment. To overcome the challenges from both automatic metrics and human evaluation, we first design specific crowdsourcing human evaluation methods for these three target tasks, respectively. We then show that these human evaluation methods are reproducible, highly reliable, easy to deploy, and cost-effective. Additionally, with the data collected from our experiments, we measure the accuracy of existing automatic metrics and analyse the potential limitations and disadvantages of the direct application of these metrics. Furthermore, in allusion to the specific features of different tasks, we provide detailed statistical analyses on the collected data to discover their underlying trends, and further give suggestions about the directions to improving systems on different aspects

Semantic Graphs for Generating Deep Questions

10.18653/v1/2020.acl-main.135Proceedings of the 2020 Annual Meeting of the Association of Computational Linguistics (ACL '20)1463-147