Spain

Emotion-cause pair extraction (ECPE), which aims at simultaneously extracting emotion-cause pairs that express emotions and their corresponding causes in a document, plays a vital role in understanding natural languages. Considering that a cause usually appears around its corresponding emotion, we construct a pair graph and a Pair Graph Convolutional Network (PairGCN) to model dependency relations among local neighborhood candidate pairs. Moreover, in our proposed graph, there are three types of dependency relations and each type of dependency relations has its own way to propagate contextual information. Experiments on a benchmark Chinese emotion-cause pair extraction corpus demonstrate the effectiveness of the proposed model.

COLING 2020

End-to-End Emotion-Cause Pair Extraction with Graph Convolutional Network

stages 

COLING, the International Conference on Computational Linguistics, is one of the premier conferences for natural language processing and computational linguistics. Often grouped within the field of artificial intelligence, but actually pre-dating the development of artificial intelligence, advances in computational linguistics and natural language processing are now some of the major drivers behind the use of artificial intelligence for commercial and social applications – for example, on-line search, machine translation and with voice-assisted conversational devices.

First established in 1965, the biennial COLING conference is held in diverse parts of the globe and attracts participants from both top-ranked research centers and emerging countries. Today, the most important developments in our field are taking place not only in universities and academic research institutes, but also in industrial research departments and in technological startups. COLING conferences provide opportunities for all these communities to showcase their exciting developments.

poster

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. 

Explanation Regeneration using Language and Graph Learning Methods

The experimental landscape in natural language processing for social media is too fragmented. Each year, new shared tasks and datasets are proposed, ranging from classics like sentiment analysis to irony detection or emoji prediction. Therefore, it is unclear what the current state of the art is, as there is no standardized evaluation protocol, neither a strong set of baselines trained on such domain- specific data. In this paper, we propose a new evaluation framework (TWEETEVAL) consist- ing of seven heterogeneous Twitter-specific classification tasks. We also provide a strong set of baselines as starting point, and com- pare different language modeling pre-training strategies. Our initial experiments show the effectiveness of starting off with existing pre- trained generic language models, and continue training them on Twitter corpora.

TweetEval: Unified Benchmark and Comparative Evaluation for Tweet Classification

It is an open question to what extent perceptions of literary quality are derived from text-intrinsic versus social factors. While supervised models can predict literary quality ratings from textual factors quite successfully, as shown in the Riddle of Literary Quality project (Koolen et al., 2020), this does not prove that social factors are not important, nor can we assume that readers make judgments on literary quality in the same way and based on the same information as machine learning models. We report the results of a pilot study to gauge the effect of textual features on literary ratings of Dutch-language novels by participants in a controlled experiment with 48 participants. In an exploratory analysis, we compare the ratings to those from the large reader survey of the Riddle in which social factors were not excluded, and to machine learning predictions of those literary ratings. We find moderate to strong correlations of questionnaire ratings with the survey ratings, but the predictions are closer to the survey ratings. Code and data: https://github.com/andreasvc/litquest

Results of a Single Blind Literary Taste Test

This paper presents AutoGOAL, a system for automatic machine learning (AutoML) that uses heterogeneous techniques. In contrast with existing AutoML approaches, our contribution can automatically build machine learning pipelines that combine techniques and algorithms from different frameworks, including shallow classifiers, natural language processing tools, and neural networks. We define the heterogeneous AutoML optimization problem as the search for the best sequence of algorithms that transforms specific input data into the desired output. 
This provides a novel theoretical and practical approach to AutoML. Our proposal is experimentally evaluated in diverse machine learning problems and compared with alternative approaches, showing that it is competitive with other AutoML alternatives in standard benchmarks. Furthermore, it can be applied to novel scenarios, such as several NLP tasks, where existing alternatives cannot be
directly deployed. The system is freely available and includes in-built compatibility with a large number of popular machine learning frameworks, which makes our approach useful for solving practical problems with relative ease and effort.

Automatic Discovery of Heterogeneous Machine Learning Pipelines: An Application to Natural Language Processing


SAP - Leveraging Chargrid and BERTgrid for document information extraction

Zalando - The Language of Fashion AI at Zalando: From Search and Customer Feedback to Outfits

To explore the potential sembanking in Korean and ways to represent the meaning of Korean sentences, this paper reports on the process of applying Abstract Meaning Representation to Korean, a semantic representation framework that has been studied in a wide range of languages, and its output: the Korean AMR corpus. The corpus which is constructed so far is a size of 1,253 sentences and its raw texts are from ExoBrain Corpus, a state-led R&D project on language AI. This paper also analyzes the result in both qualitative and quantitative manners, proposing discussions for further development.

Building Korean Abstract Meaning Representation Corpus

The computational linguistics of conversation modeling

We introduce a new dataset for training and evaluating grounded language models. Our data is collected within a virtual reality environment and is designed to emulate the quality of language data to which a pre-verbal child is likely to have access: That is, naturalistic, spontaneous speech paired with richly grounded visuospatial context. We use the collected data to compare several distributional semantics models for verb learning. We evaluate neural models based on 2D (pixel) features as well as feature-engineered models based on 3D (symbolic, spatial) features, and show that neither modeling approach achieves satisfactory performance. Our results are consistent with evidence from child language acquisition that emphasizes the difficulty of learning verbs from naive distributional data. We discuss avenues for future work on cognitively-inspired grounded language learning, and release our corpus with the intent of facilitating research on the topic.

End-to-End Emotion-Cause Pair Extraction with Graph Convolutional Network

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Explanation Regeneration using Language and Graph Learning Methods

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End-to-End Emotion-Cause Pair Extraction with Graph Convolutional Network

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Next from COLING 2020

Explanation Regeneration using Language and Graph Learning Methods

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A Review of dataset and labeling methods for causality extraction

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PRESENTATIONS

CONFERENCES

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RESOURCES

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