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Estimating Subjective Crowd-Evaluations as an Additional Objective to Improve natural language Generation 1

Estimating Subjective Crowd-Evaluations as an Additional Obj...

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1st workshop on Human Evaluation of NLP Systems, HumEval 2021

作者： Nyberg, Jakob Manuvinakurike, Ramesh Paetzel-Prüsmann, Maike KTH Royal Institute of Technology Sweden Intel Labs United States University of Potsdam Germany

ISBN: (纸本)9781954085107

Human ratings are one of the most prevalent methods to evaluate the performance of natural language processing algorithms. Similarly, it is common to measure the quality of sentences generated by a natural language generation model using human raters. In this paper, we argue for exploring the use of subjective evaluations within the process of training language generation models in a multi-task learning setting. As a case study, we use a crowd-authored dialogue corpus to fine-tune six different language generation models. Two of these models incorporate multi-task learning and use subjective ratings of lines as part of an explicit learning goal. A human evaluation of the generated dialogue lines reveals that utterances generated by the multi-tasking models were subjectively rated as the most typical, most moving the conversation forward, and least offensive. based on these promising first results, we discuss future research directions for incorporating subjective human evaluations into language model training and to hence keep the human user in the loop during the development process. © 2021 Association for Computational Linguistics.

关键词： Learning systems

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A Multi-Level Attention Model for Evidence-based Fact Checking

A Multi-Level Attention Model for Evidence-Based Fact Checki...

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Joint Conference of 59th Annual Meeting of the Association-for-Computational-Linguistics (ACL) / 11th International Joint Conference on natural language processing (IJCNLP) / 6th workshop on Representation Learning for NLP (RepL4NLP)

作者： Kruengkrai, Canasai Yamagishi, Junichi Wang, Xin Natl Inst Informat Tokyo Japan

ISBN: (纸本)9781954085541

Evidence-based fact checking aims to verify the truthfulness of a claim against evidence extracted from textual sources. Learning a representation that effectively captures relations between a claim and evidence can be challenging. Recent state-of-the-art approaches have developed increasingly sophisticated models based on graph structures. We present a simple model that can be trained on sequence structures. Our model enables inter-sentence attentions at different levels and can benefit from joint training. Results on a large-scale dataset for Fact Extraction and VERification (FEVER) show that our model outperforms the graph-based approaches and yields 1.09% and 1.42% improvements in label accuracy and FEVER score, respectively, over the best published model.

关键词： graphic methods

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A graph based semi-supervised approach for analysis of derivational nouns in Sanskrit 11

A graph based semi-supervised approach for analysis of deriv...

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11th workshop on graph-based methods for natural language processing, Textgraphs 2017, in conjunction with the 55th Annual Meeting of the Association for Computational Linguistics, ACL 2017

作者： Krishna, Amrith Satuluri, Pavankumar Ponnada, Harshavardhan Ahmed, Muneeb Arora, Gulab Hiware, Kaustubh Goyal, Pawan School of Linguistics and Literary Studies Chinmaya Vishwavidyapeeth CEG Campus India Dept. of Electrical Engineering Indian Institute of Technology BHU India Dept. of Computer Science and Engineering Indian Institute of Technology Kharagpur India

ISBN: (纸本)9781945626609

Derivational nouns are widely used in Sanskrit corpora and is a prevalent means of productivity in the language. Currently there exists no analyser that identifies the derivational nouns. We propose a semi supervised approach for identification of derivational nouns in Sanskrit. We not only identify the derivational words, but also link them to their corresponding source words. The novelty of our work is primarily in its design of the network structure for the task. The edge weights are featurised based on the phonetic, morphological, syntactic and the semantic similarity shared between the words to be identified. We find that our model is effective for the task, even when we employ a labelled dataset which is only 5 % to that of the entire dataset. © 2017 Association for Computational Linguistics

关键词： graphic methods

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Joint Learning of the graph and the Data Representation for graph-based Semi-Supervised Learning 14

Joint Learning of the Graph and the Data Representation for ...

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14th workshop on graph-based methods for natural language processing, Textgraphs 2020, in conjunction with the 28th International Conference on Computational Linguistics, COLING 2020

作者： Vargas-Vieyra, Mariana Bellet, Aurélien Denis, Pascal Magnet Team Inria Lille - Nord Europe France Université de Lille CNRS UMR 9189 CRIStAL Villeneuve d’Ascq59650 France

ISBN: (纸本)9781952148422

graph-based semi-supervised learning is appealing when labels are scarce but large amounts of unlabeled data are available. These methods typically use a heuristic strategy to construct the graph based on some fixed data representation, independently of the available labels. In this paper, we propose to jointly learn a data representation and a graph from both labeled and unlabeled data such that (i) the learned representation indirectly encodes the label information injected into the graph, and (ii) the graph provides a smooth topology with respect to the transformed data. Plugging the resulting graph and representation into existing graph-based semi-supervised learning algorithms like label spreading and graph convolutional networks, we show that our approach outperforms standard graph construction methods on both synthetic data and real datasets. © COLING *** rights reserved.

关键词： graphic methods

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PGL at Textgraphs 2020 Shared Task: Explanation Regeneration using language and graph Learning methods 14

PGL at TextGraphs 2020 Shared Task: Explanation Regeneration...

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14th workshop on graph-based methods for natural language processing, Textgraphs 2020, in conjunction with the 28th International Conference on Computational Linguistics, COLING 2020

作者： Li, Weibin Lu, Yuxiang Huang, Zhengjie Liu, Jiaxiang Su, Weiyue Feng, Shikun Sun, Yu Baidu Inc. China

ISBN: (纸本)9781952148422

This paper describes the system designed by the Baidu PGL Team which achieved the first place in the Textgraphs 2020 Shared Task. The task focuses on generating explanations for elementary science questions. Given a question and its corresponding correct answer, we are asked to select the facts that can explain why the answer is correct for that question and answering (QA) from a large knowledge base. To address this problem, we use a pre-trained language model to recall the top-K relevant explanations for each question. Then, we adopt a re-ranking approach based on a pre-trained language model to rank the candidate explanations. To further improve the rankings, we also develop an architecture consisting both powerful pre-trained transformers and GNNs to tackle the multi-hop inference problem. The official evaluation shows that, our system can outperform the second best system by 1.91 points. © COLING *** rights reserved.

关键词： Knowledge based systems

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LNN-EL: A Neuro-Symbolic Approach to Short-text Entity Linking 59

LNN-EL: A Neuro-Symbolic Approach to Short-text Entity Linki...

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作者： Jiang, Hang Gurajada, Sairam Lu, Qiuhao Neelam, Sumit Popa, Lucian Sen, Prithviraj Li, Yunyao Gray, Alexander MIT Cambridge MA 02139 USA IBM Res Yorktown Hts NY USA Univ Oregon Eugene OR 97403 USA

ISBN: (纸本)9781954085527

Entity linking (EL), the task of disambiguating mentions in text by linking them to entities in a knowledge graph, is crucial for text understanding, question answering or conversational systems. Entity linking on short text (e.g., single sentence or question) poses particular challenges due to limited context. While prior approaches use either heuristics or black-box neural methods, here we propose LNNEL, a neuro-symbolic approach that combines the advantages of using interpretable rules based on first-order logic with the performance of neural learning. Even though constrained to using rules, LNN-EL performs competitively against SotA black-box neural approaches, with the added benefits of extensibility and transferability. In particular, we show that we can easily blend existing rule templates given by a human expert, with multiple types of features (priors, BERT encodings, box embeddings, etc), and even scores resulting from previous EL methods, thus improving on such methods. For instance, on the LC-QuAD-1.0 dataset, we show more than 4% increase in F1 score over previous SotA. Finally, we show that the inductive bias offered by using logic results in learned rules that transfer well across datasets, even without fine tuning, while maintaining high accuracy.

关键词： Heuristic methods

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Safeguarding against spurious AI-based predictions: The case of automated verbal memory assessment 7

Safeguarding against spurious AI-based predictions: The case...

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7th workshop on Computational Linguistics and Clinical Psychology: Improving Access, CLPsych 2021

作者： Chandler, Chelsea Foltz, Peter W. Cohen, Alex S. Holmlund, Terje B. Elvevåg, Brita Department Of Computer Science And Institute Of Cognitive Science University Of Colorado Boulder United States Department Of Psychology And Center For Computation And Technology Louisiana State University United States Department Of Clinical Medicine University Of Tromsø - Arctic University Of Norway Norway Norwegian Centre For EHealth Research Arctic University Of Norway Norway

ISBN: (纸本)9781954085411

A growing amount of psychiatric research incorporates machine learning and natural language processing methods, however findings have yet to be translated into actual clinical decision support systems. Many of these studies are based on relatively small datasets in homogeneous populations, which has the associated risk that the models may not perform adequately on new data in real clinical practice. The nature of serious mental illness is that it is hard to define, hard to capture, and requires frequent monitoring, which leads to imperfect data where attribute and class noise are common. With the goal of an effective AI-mediated clinical decision support system, there must be computational safeguards placed on the models used in order to avoid spurious predictions and thus allow humans to review data in the settings where models are unstable or bound not to generalize. This paper describes two approaches to implementing safeguards: (1) the determination of cases in which models are unstable by means of attribute and class based outlier detection and (2) finding the extent to which models show inductive bias. These safeguards are illustrated in the automated scoring of a story recall task via natural language processing methods. With the integration of human-in-the-loop machine learning in the clinical implementation process, incorporating safeguards such as these into the models will offer patients increased protection from spurious predictions. ©2021 Association for Computational Linguistics.

关键词： natural language processing systems

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Resolving Implicit References in Instructional Texts 2

Resolving Implicit References in Instructional Texts

引用

2nd workshop on Computational Approaches to Discourse, CODI 2021

作者： Anthonio, Talita Rani Roth, Michael University of Stuttgart Institute for Natural Language Processing Germany

ISBN: (纸本)9781954085992

The usage of (co-)referring expressions in discourse contributes to the coherence of a text. However, text comprehension can be difficult when referring expressions are non-verbalized and have to be resolved in the discourse context. In this paper, we propose a novel dataset of such implicit references, which we automatically derive from insertions of references in collaboratively edited how-to guides. Our dataset consists of 6,014 instances, making it one of the largest datasets of implicit references and a useful starting point to investigate misunderstandings caused by underspecified language. We test different methods for resolving implicit references in our dataset based on the Generative Pre-trained Transformer model (GPT) and compare them to heuristic baselines. Our experiments indicate that GPT can accurately resolve the majority of implicit references in our data. Finally, we investigate remaining errors and examine human preferences regarding different resolutions of an implicit reference given the discourse context. © 2021 Association for Computational Linguistics.

关键词： Heuristic methods

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methods and algorithms of the analytical platform for analyzing the labor market and the compliance of the higher education system with market needs 6

Methods and algorithms of the analytical platform for analyz...

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6th International workshop on Deep Learning in Computational Physics, DLCP 2022

作者： Belov, S.D. Filozova, I.A. Gavrilenko, Y.E. Ilina, A.V. Javadzade, J.N. Kadochnikov, I.S. Korenkov, V.V. Pelevanyuk, I.S. Priakhina, D.I. Semenov, R.N. Tarabrin, V.А. Zrelov, P.V. Joint Institute for Nuclear Research Meshcheryakov Laboratory of Information Technologies Joliot-Curie St. Moscow Region Dubna141980 Russia Plekhanov Russian University of Economics Stremyanny lane 36 Moscow117997 Russia

The article discusses the methods and algorithms that underlie the analytical platform for automated monitoring and analysis of the labor market in the Russian Federation, as well as the analysis of the higher education system's compliance with the labor market's current needs. The study involved natural language processing methods and Big Data technologies. The general scheme corresponds to end-to-end processing - from data collection and storage, their transformation, analysis, and modeling, to visualization of results and decision-making. The analytical core of the system is a module for intellectual analysis of the texts of job advertisements in the labor market. The vacancies are collected from the most complete databases in Russia (namely HeadHunter, Work in Russia and SuperJob). Job descriptions of vacancies are matched with the official list of professions of the Ministry of Labor and Social Protection of Russia using semantic analysis based on neural models trained on large arrays of texts. Also, using semantic analysis, automated monitoring and intellectual analysis of the staffing needs of the all-Russian and regional labor markets are carried out according to the range of specialties of the university. Data gathering has been ongoing from 2015 up to now. © Copyright owned by the author(s) under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (CC BY-NC-ND 4.0)

关键词： Big data

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Emotion Classification in German Plays with Transformer-based language Models Pretrained on Historical and Contemporary language 5

Emotion Classification in German Plays with Transformer-base...

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5th Joint SIGHUM workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities and Literature, LaTeCHCLfL 2021

作者： Schmidt, Thomas Dennerlein, Katrin Wolff, Christian Media Informatics Group University of Regensburg Germany German Literary Studies University of Würzburg Germany

ISBN: (纸本)9781954085916

We present results of a project on emotion classification on historical German plays of Enlightenment, Storm and Stress, and German Classicism. We have developed a hierarchical annotation scheme consisting of 13 sub-emotions like suffering, love and joy that sum up to 6 main and 2 polarity classes (positive/negative). We have conducted textual annotations on 11 German plays and have acquired over 13,000 emotion annotations by two annotators per play. We have evaluated multiple traditional machine learning approaches as well as transformer-based models pretrained on historical and contemporary language for a single-label text sequence emotion classification for the different emotion categories. The evaluation is carried out on three different instances of the corpus: (1) taking all annotations, (2) filtering overlapping annotations by annotators, (3) applying a heuristic for speech-based analysis. Best results are achieved on the filtered corpus with the best models being large transformer-based models pretrained on contemporary German language. For the polarity classification accuracies of up to 90% are achieved. The accuracies become lower for settings with a higher number of classes, achieving 66% for 13 sub-emotions. Further pretraining of a historical model with a corpus of dramatic texts led to no improvements. © LaTeCHCLfL,EMNLP 2021 - *** rights reserved.

关键词： natural language processing systems

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