Investigating Pretrained Language Models for Graph-to-Text Generation

Graph-to-text generation aims to generate fluent texts from graph-based data. In this paper, we investigate two recently proposed pretrained language models (PLMs) and analyze the impact of different task-adaptive pretraining strategies for PLMs in graph-to-text generation. We present a study across three graph domains: meaning representations, Wikipedia knowledge graphs (KGs) and scientific KGs. We show that the PLMs BART and T5 achieve new state-of-the-art results and that task-adaptive pretraining strategies improve their performance even further. In particular, we report new state-of-the-art BLEU scores of 49.72 on LDC2017T10, 59.70 on WebNLG, and 25.66 on AGENDA datasets - a relative improvement of 31.8%, 4.5%, and 42.4%, respectively. In an extensive analysis, we identify possible reasons for the PLMs' success on graph-to-text tasks. We find evidence that their knowledge about true facts helps them perform well even when the input graph representation is reduced to a simple bag of node and edge labels.Comment: Our code and pretrained model checkpoints are available at https://github.com/UKPLab/plms-graph2tex

Modeling Graph Structure via Relative Position for Text Generation from Knowledge Graphs

We present Graformer, a novel Transformer-based encoder-decoder architecture for graph-to-text generation. With our novel graph self-attention, the encoding of a node relies on all nodes in the input graph - not only direct neighbors - facilitating the detection of global patterns. We represent the relation between two nodes as the length of the shortest path between them. Graformer learns to weight these node-node relations differently for different attention heads, thus virtually learning differently connected views of the input graph. We evaluate Graformer on two popular graph-to-text generation benchmarks, AGENDA and WebNLG, where it achieves strong performance while using many fewer parameters than other approaches

Efficient determination of cysteine sulphoxides in Allium plants applying new biosensor and HPLC-MS² methods

Cysteine sulphoxide (CSO) contents of 16 different accessions of garlic (Allium sativum L.) and 15 varieties of onion (Allium cepa L.) were measured using two different rapid analytical methods: a biosensoric approach and a newly developed HPLC-MS2 technique. Both methods allow quantification of naturally occurring cysteine sulphoxides present in Allium plants without time-consuming sample pretreatment such as derivatisation of amino acid derivatives prior to HPLC-separation. It has been found that the amount of alliin, which is the predominant CSO occurring in garlic, varies between 0.2 and 2.2 g/100 g dry matter. Contrary to that, isoalliin representing the main CSO in onion has been detected in significantly lower amounts (0.3 to 1.25 g/100 g dry matter)

Identifying automatically generated headlines using transformers

False information spread via the internet and social media influences public opinion and user activity, while generative models enable fake content to be generated faster and more cheaply than had previously been possible. In the not so distant future, identifying fake content generated by deep learning models will play a key role in protecting users from misinformation. To this end, a dataset containing human and computer-generated headlines was created and a user study indicated that humans were only able to identify the fake headlines in 47.8% of the cases. However, the most accurate automatic approach, transformers, achieved an overall accuracy of 85.7%, indicating that content generated from language models can be filtered out accurately

The ‘Galilean Style in Science’ and the Inconsistency of Linguistic Theorising

Chomsky’s principle of epistemological tolerance says that in theoretical linguistics contradictions between the data and the hypotheses may be temporarily tolerated in order to protect the explanatory power of the theory. The paper raises the following problem: What kinds of contradictions may be tolerated between the data and the hypotheses in theoretical linguistics? First a model of paraconsistent logic is introduced which differentiates between week and strong contradiction. As a second step, a case study is carried out which exemplifies that the principle of epistemological tolerance may be interpreted as the tolerance of week contradiction. The third step of the argumentation focuses on another case study which exemplifies that the principle of epistemological tolerance must not be interpreted as the tolerance of strong contradiction. The reason for the latter insight is the unreliability and the uncertainty of introspective data. From this finding the author draws the conclusion that it is the integration of different data types that may lead to the improvement of current theoretical linguistics and that the integration of different data types requires a novel methodology which, for the time being, is not available

The ‘Galilean Style in Science’ and the Inconsistency of Linguistic Theorising

Chomsky’s principle of epistemological tolerance says that in theoretical linguistics contradictions between the data and the hypotheses may be temporarily tolerated in order to protect the explanatory power of the theory. The paper raises the following problem: What kinds of contradictions may be tolerated between the data and the hypotheses in theoretical linguistics? First a model of paraconsistent logic is introduced which differentiates between week and strong contradiction. As a second step, a case study is carried out which exemplifies that the principle of epistemological tolerance may be interpreted as the tolerance of week contradiction. The third step of the argumentation focuses on another case study which exemplifies that the principle of epistemological tolerance must not be interpreted as the tolerance of strong contradiction. The reason for the latter insight is the unreliability and the uncertainty of introspective data. From this finding the author draws the conclusion that it is the integration of different data types that may lead to the improvement of current theoretical linguistics and that the integration of different data types requires a novel methodology which, for the time being, is not available

Using audio stimuli in acceptability judgment experiments

In this paper, we argue that moving away from written stimuli in acceptability judgment experiments is necessary to address the systematic exclusion of particular empirical phenomena, languages/varieties, and speakers in psycholinguistics. We provide user‐friendly guidelines for conducting acceptability experiments which use audio stimuli in three platforms: Praat, Qualtrics, and PennController for Ibex. In supplementary materials, we include data and R script from a sample experiment investigating English constituent order using written and audio stimuli. This paper aims not only to increase the types of languages, speakers, and phenomena which are included in experimental syntax, but also to help researchers who are interested in conducting experiments to overcome the initial learning curve. Video Abstract link: https://www.youtube.com/watch?v=GoWYY1O9ugsPeer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/156434/2/lnc312377_am.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/156434/1/lnc312377.pd

Разработка состава совместно вжигаемых металлизационных паст для алюмонитридной керамики

Целью выпускной работы является разработка состава металлизационной пасты на основе порошков вольфрама для совместного вжигания с алюмонитридной керамикой. В работе приведены общие сведения о металлизированных покрытиях и AlN керамике, содержатся обработанные результаты по приготовлению металлизационных паст, нанесении их на поверхность керамической ленты, совместном вжигании и последующем изучении структуры и свойств полученных образцов.The aim of the final work is to develop the composition of metallization paste based on tungsten powders for co-firing with aluminitride ceramics. The work provides general information about metallized coatings and AlN ceramics, contains processed results on the preparation of metallization pastes, applying them to the surface of a ceramic tape, combined firing and subsequent study of the structure and properties of the samples obtained