Spain

To learn about the major problems, we provide a short but comprehensive survey based on two complementary studies: (1) a literature review where we collect and organise problems known from interviews with workers, and (2) an empirical data analysis where we use topic modelling to mine workers’ complaints from a new English corpus of workers’ forum discussions.

COLING 2020

Mining Crowdsourcing Problems from Discussion Forums of Workers

workers' problems

topic models

crowdsourcing

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.

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.

poster

Named Entity Recognition (NER) is a fundamental Natural Language Processing (NLP) task and has remained an active research field. In recent years, transformer models and more specifically the BERT model developed at Google revolutionised the field of NLP. While the performance of transformer-based approaches such as BERT has been studied for NER, there has not yet been a study for the fine-grained Named Entity Recognition (FG-NER) task. In this paper, we compare three transformer-based models (BERT, RoBERTa, and XLNet) to two non-transformer-based models (CRF and BiLSTM-CNN-CRF). Furthermore, we apply each model to a multitude of distinct domains. We find that transformer-based models incrementally outperform the studied non-transformer-based models in most domains with respect to the F1 score. Furthermore, we find that the choice of domain significantly influenced the performance regardless of the respective data size or the model chosen.

Evaluating Pretrained Transformer-based Models on the Task of Fine-Grained Named Entity Recognition

Building robust natural language understanding systems will require a clear characterization of whether and how various linguistic meaning representations complement each other. To perform a systematic comparative analysis, we evaluate the mapping between meaning representations from different frameworks using two complementary methods: (i) a rule-based converter, and (ii) a supervised delexicalized parser that parses to one framework using only information from the other as features. We apply these methods to convert the STREUSLE corpus (with syntactic and lexical semantic annotations) to UCCA (a graph-structured full-sentence meaning representation). Both methods yield surprisingly accurate target representations, close to fully supervised UCCA parser quality—indicating that UCCA annotations are partially redundant with STREUSLE annotations. Despite this substantial convergence between frameworks, we find several important areas of divergence.

Comparison by Conversion: Reverse-Engineering UCCA from Syntax and Lexical Semantics

Existing end-to-end task-oriented dialog systems struggle to dynamically model long dialog context for interactions and effectively incorporate knowledge base (KB) information into dialog generation. To conquer these limitations, we propose a Dual Dynamic Memory Network (DDMN) for multi-turn dialog generation, which maintains two core components: dialog memory manager and KB memory manager. The dialog memory manager dynamically expands the dialog memory turn by turn and keeps track of dialog history with an updating mechanism, which encourages the model to filter irrelevant dialog history and memorize important newly coming information. The KB memory manager shares the structural KB triples throughout the whole conversation, and dynamically extracts KB information with a memory pointer at each turn. Experimental results on three benchmark datasets demonstrate that DDMN significantly outperforms the strong baselines in terms of both automatic evaluation and human evaluation.

Dual Dynamic Memory Network for End-to-End Multi-turn Task-oriented Dialog Systems

Generating knowledge from natural language data has aided in solving many artificial intelligence problems. Vector representations of words have been the driving force behind the majority of natural language processing tasks. This paper develops a novel approach for predicting the conservation status of animal species using custom generated scientific name embeddings. We use two different vector embeddings generated using representation learning on Wikipedia text and animal taxonomy data. We generate name embeddings for all species in the animal kingdom using unsupervised learning and build a model on the IUCN Red List dataset to classify species into endangered or least-concern. To our knowledge, this is the first work that makes use of learnt features instead of handcrafted features for this task and achieves competitive results. Based on the high confidence results of our model, we also predict the conservation status of data deficient species whose conservation status is still unknown, steering more focus towards them for protection. These embeddings have also been made publicly available. We believe this will greatly help in solving various downstream tasks and further advance research in the cross-domain involving natural language processing, conservation biology, and life sciences.

A Representation Learning Approach to Animal Biodiversity Conservation

Research into the area of multiparty dialog has grown considerably over recent years. We present the Molweni dataset, a machine reading comprehension (MRC) dataset with discourse structure built over multiparty dialog. Molweni's source samples from the Ubuntu Chat Corpus, including 10,000 dialogs comprising 88,303 utterances. We annotate 30,066 questions on this corpus, including both answerable and unanswerable questions. Molweni also uniquely contributes discourse dependency annotations in a modified Segmented Discourse Representation Theory (SDRT; Asher et al., 2016) style for all of its multiparty dialogs, contributing large-scale (78,245 annotated discourse relations) data to bear on the task of multiparty dialog discourse parsing. Our experiments show that Molweni is a challenging dataset for current MRC models: BERT-wwm, a current, strong SQuAD 2.0 performer, achieves only 67.7% F1 on Molweni's questions, a 20+% significant drop as compared against its SQuAD 2.0 performance.

Molweni: A Challenge Multiparty Dialogue-based Machine Reading Comprehension Dataset with Discourse Structure

Script learning aims to predict the subsequent event according to the existing event chain. Recent studies focus on event co-occurrence to solve this problem. However, few studies integrate external event knowledge to solve this problem. With our observations, external event knowledge can provide additional knowledge like temporal or causal knowledge for understanding event chain better and predicting the right subsequent event. In this work, we integrate event knowledge
from ASER (Activities, States, Events and their Relations) knowledge base to help predict the next event. We propose a new approach consisting of knowledge retrieval stage and knowledge integration stage. In the knowledge retrieval stage, we select relevant external event knowledge from ASER. In the knowledge integration stage, we propose three methods to integrate external knowledge into our model and infer final answers. Experiments on the widely-used MultiChoice Narrative Cloze (MCNC) task show our approach achieves state-of-the-art performance compared to other methods.

Integrating External Event Knowledge for Script Learning

Sentiment analysis provides a useful overview of customer review contents. Many review websites allow a user to enter a summary in addition to a full review. Intuitively, summary information may give additional benefit for review sentiment analysis. In this paper, we conduct a study to exploit methods for better use of summary information. We start by finding out that the sentimental signal distribution of a review and that of its corresponding summary are in fact complementary to each other. We thus explore various architectures to better guide the interactions between the two and propose a hierarchically-refined review-centric attention model. Empirical results show that our review-centric model can make better use of user-written summaries for review sentiment analysis, and is also more effective compared to existing methods when the user summary is replaced with summary generated by an automatic summarization system.

Making the Best Use of Review Summary for Sentiment Analysis

Event extraction (EE) plays an important role in real-life applications, such as information retrieval and news summarization. It aims to discover events with triggers and their corresponding arguments. Typically, EE contains several subtasks: trigger identification, trigger classification, event argument identification, and argument role classification. Most existing work follows a pipelined manner or uses a joint structure but is pipelined in essence. As a result, these efforts fail to utilize information interactions among event triggers, event arguments, and argument roles, which causes information redundancy. In this paper, we propose to exploit the role information of the arguments in an event and devise a Hierarchical Policy Network (HPNet) to perform joint EE. The whole EE process is fulfilled through a two-level hierarchical structure consisting of two policy networks for event detection and argument detection. The deep information interactions among the subtasks are realized, and it is more natural to deal with multiple events issue. 


Joint Event Extraction with Hierarchical Policy Network

We present a multi-level alignment pretraining method in a unified architecture for multi-lingual semantic parsing. In this architecture, we use an adversarial training method to align the space of different languages and use sentence level and word level parallel corpus as supervision information to align the semantic of different languages. Finally, we jointly train the multi-level alignment and semantic parsing tasks. We conduct experiments on a publicly avail-able multi-lingual semantic parsing dataset ATIS and a newly constructed dataset. Experimental results show that our model outperforms state-of-the-art methods on both datasets.


Multi-level Alignment Pretraining for Multi-lingual Semantic Parsing

Open-domain reading comprehension is a challenging issue in terms of natural language understanding. In this paper, we proposes a miscellaneous-context-based
method to convert a sentence into a knowledge embedding in the form of a directed graph. Based on the idea of conceptual graphs, this graph representation method
abbreviated as AGG-CG can (1) accommodate the slot-filling challenges in typical question answering, (2) access to the sentence-level graph structure in order to explicitly capture the neighbouring connections of reference concept nodes, and (3) project an edge of a sentence-level graph to the space of semantic relevance with respect to the corresponding concept nodes, by cooperating with the semantics-measured module. In conclusion, our approach is the first towards the interpretable process of learning human-comprehensible graphical representations with the experimental evidence.

Downloads

Next from COLING 2020

Evaluating Pretrained Transformer-based Models on the Task of Fine-Grained Named Entity Recognition

Similar lecture

Grammatical error detection in transcriptions of spoken English

.css-70qvj9{display:-webkit-box;display:-webkit-flex;display:-ms-flexbox;display:flex;-webkit-align-items:center;-webkit-box-align:center;-ms-flex-align:center;align-items:center;}Downloads

Next from COLING 2020

Evaluating Pretrained Transformer-based Models on the Task of Fine-Grained Named Entity Recognition

Similar lecture

Grammatical error detection in transcriptions of spoken English

Downloads