Korea, Republic of

In this presentation, we introduce a novel dual graph-based model, Uncertainty-aware Propagation Structure Reconstruction (UPSR) for improving fake news detection. Specifically, after the original propagation modeling, we introduce propagation structure reconstruction to fully explore latent interactions in the actual propagation. We design a novel Gaussian Propagation Estimation to refine the original deterministic node representation by multiple Gaussian distributions and arise latent interactions with KL divergence between distributions in a multi-facet manner. Extensive experiments on two real-world datasets demonstrate the effectiveness and superiority of our model.

COLING 2022

Uncertainty-aware Propagation Structure Reconstruction for Fake News Detection

information propagation

fake news detection

graph neural network

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### THE CONFERENCE WE KNOW AND WE WANT.
**COLING**, the International Conference on Computational Linguistics, is one of the premier conferences for the natural language processing and computational linguistics.

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 including tech-startups. COLING provides opportunities for all these communities to showcase their exciting discovery.

In fall of 2022, COLING will be held in Gyeongju in a hybrid format. All participants can either present at the venue site or join virtually. As more people get vaccinated, we are happy to provide safer environments for our colleagues. We believe that COLING 2022 will be one of the conferences, free from the pandemic. The hybrid format gives presenters and sponsors a valuable opportunity to promote their companies in both an online and in-person venue. For the first time in a long time, customers can interact with their sponsor's products first-hand. The online venue, too, gives sponsors the chance to network with those unable to attend the in-person session.

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COLING, the International Conference on Computational Linguistics, is one of the premier conferences for the natural language processing and computational linguistics.

poster

Heterogeneous Graph Neural Networks (HeterGNN) have been recently introduced as an emergent approach for extracting document summarization (EDS) by exploiting the cross-relations between words and sentences. However, applying HeterGNN for long documents is still an open research issue. One of the main majors is the lacking of inter-sentence connections. In this regard, this paper exploits how to apply HeterGNN for long documents by building a graph on sentence-level nodes (homogeneous graph) and combine with HeterGNN for capturing the semantic information in terms of both inter and intra-sentence connections. Experiments on two benchmark datasets of long documents such as PubMed and ArXiv show that our method is able to achieve state-of-the-art results in this research field.

Multi Graph Neural Network for Extractive Long Document Summarization

Annotation conversion is an effective way to construct datasets under new annotation guidelines based on existing datasets with little human labour. Previous work has been limited in conversion between tree-structured datasets and mainly focused on feature-based models which are not easily applicable to new conversions. In this paper, we propose two simple and effective graph-to-graph annotation conversion approaches, namely Label Switching and Graph2Graph Linear Transformation, which use pseudo data and inherit parameters to guide graph conversions respectively. These methods are able to deal with conversion between graph-structured annotations and require no manually designed features. To verify their effectiveness, we manually construct a graph-structured parallel annotated dataset and evaluate the proposed approaches on it as well as other existing parallel annotated datasets. Experimental results show that the proposed approaches outperform strong baselines with higher conversion scores. To further validate the quality of converted graphs, we utilize them to train the target parser and find graphs generated by our approaches lead to higher parsing scores than those generated by the baselines.

Simple and Effective Graph-to-Graph Annotation Conversion

Document-level biomedical relation extraction (Bio-DocuRE) is an important branch of biomedical text mining that aims to automatically extract all relation facts from the biomedical text. Since there are a considerable number of relations in biomedical documents that need to be judged by other existing relations, logical reasoning has become a research hotspot in the past two years. However, current models with reasoning are single-granularity only based on one element information, ignoring the complementary fact of different granularity reasoning information. In addition, obtaining rich document information is a prerequisite for logical reasoning, but most of the previous models cannot sufficiently utilize document information, which limits the reasoning ability of the model. In this paper, we propose a novel Bio-DocuRE model called FILR. Specifically, FILR presents a multi-dimensional information fusion module MDIF to extract sufficient global document information. Then FILR proposes a multi-granularity reasoning module MGLR to obtain rich inference information through the reasoning of both entity-pairs and mention-pairs. We evaluate our FILR model on two widely used biomedical corpora CDR and GDA. Experimental results show that FILR achieves state-of-the-art performance.

Document-level Biomedical Relation Extraction Based on Multi-Dimensional Fusion Information and Multi-Granularity Logical Reasoning

Recently, a conversational question answering system has been introduced in various fields. However, building robust CQA systems requires large domain-specific datasets, and obtaining such datasets is very expensive and time consuming. In this presentation, we will introduce a novel framework that automatically generates conversational question answering data. The framework extracts question-worthy phrases from a passage and then generates corresponding questions considering previous conversations. In particular, our framework revises the extracted answers after generating questions so that answers exactly match paired questions. Experimental results prove that our framework can be utilized to construct CQA models for new domains.

Conversational QA Dataset Generation with Answer Revision

Contingent reasoning is one of the essential abilities in natural language understanding, and many language resources annotated with contingent relations have been constructed.
However, despite the recent advances in deep learning, the task of contingent reasoning is still difficult for computers.
In this study, we focus on the reasoning of contingent relation between basic events.
Based on the existing data construction method, we automatically generate large-scale pseudo-problems and incorporate the generated data into training.
We also investigate the generality of contingent knowledge through quantitative evaluation by performing transfer learning on the related tasks: discourse relation analysis, the Japanese Winograd Schema Challenge, and the JCommonsenseQA.
The experimental results show the effectiveness of utilizing pseudo-problems for both the commonsense contingent reasoning task and the related tasks, which suggests the importance of contingent reasoning.

Improving Commonsense Contingent Reasoning by Pseudo-data and its Application to the Related Tasks

In a hate speech detection model, we should consider two critical aspects in addition to detection performance–bias and explainability. Hate speech cannot be identified based solely on the presence of specific words; the model should be able to reason like humans and be explainable. To improve the performance concerning the two aspects, we propose Masked Rationale Prediction (MRP) as an intermediate task. MRP is a task to predict the masked human rationales–snippets of a sentence that are grounds for human judgment–by referring to surrounding tokens combined with their un- masked rationales. As the model learns its reasoning ability based on rationales by MRP, it performs hate speech detection robustly in terms of bias and explainability. The proposed method generally achieves state-of-the-art performance in various metrics, demonstrating its effectiveness for hate speech detection. Warning: This paper contains samples that may be upsetting.

Why is it hate speech? Masked Rationale Prediction for Explainable Hate Speech Detection

The joint multiple Intent Detection (ID) and Slot Filling (SF) is a significant challenge in spoken language understanding. Because the slots in an utterance may relate to multi-intents, most existing approaches focus on utilizing task-specific components to capture the relations between intents and slots. The customized networks restrict models from modeling commonalities between tasks and generalization for broader applications. To ad- dress the above issue, we propose a Unified Generative framework (UGEN) based on a prompt-based paradigm, and formulate the task as a question-answering problem. Specifically, we design 5-type templates as instructional prompts, and each template includes a question that acts as the driver to teach UGEN to grasp the paradigm, options that list the candidate intents or slots to reduce the answer search space, and the context denotes original utterance. Through the instructional prompts, UGEN is guided to understand in- tents, slots, and their implicit correlations. On two popular multi-intent benchmark datasets, experimental results demonstrate that UGEN achieves new SOTA performances on full-data and surpasses the baselines by a large margin on 5-shot (28.1%) and 10-shot (23%) scenarios, which verify that UGEN is robust and effective.

Incorporating Instructional Prompts into A Unified Generative Framework for Joint Multiple Intent Detection and Slot Filling

We propose a method to distill language-agnostic meaning embeddings from multilingual sentence encoders for unsupervised quality estimation of machine translation.
Our method facilitates that the meaning embeddings focus on semantics by adversarial training that attempts to eliminate language-specific information.
Experimental results on unsupervised quality estimation reveal that our method achieved higher correlations with human evaluations.

Adversarial Training on Disentangling Meaning and Language Representations for Unsupervised Quality Estimation

Accurate fact veriﬁcation depends on performing ﬁne-grained reasoning over crucial entities by capturing their latent logical relations hidden in multiple evidence clues, which is generally lacking in existing fact veriﬁcation models. In this work, we propose a novel Globalto-Local Aggregation and Fission network (GLAF) to ﬁll this gap. Instead of treating entire sentences or all semantic elements within them as nodes to construct a coarse-grained or unstructured evidence graph as in previous methods, GLAF constructs a ﬁne-grained and structured evidence graph by parsing the rambling sentences into structural triple-level reasoning clues and regarding them as graph nodes to achieve ﬁne-grained and interpretable evidence graph reasoning. Speciﬁcally, to capture latent logical relations between the clues, GLAF ﬁrst employs a local ﬁssion reasoning layer to conduct ﬁne-grained multi-hop reasoning, and then uses a global evidence aggregation layer to achieve information sharing and the interchange of evidence clues for ﬁnal claim label prediction. Experimental results on the FEVER dataset demonstrate the effectiveness of GLAF, showing that it achieves the state-of-the-art performance.

GLAF: Global-to-Local Aggregation and Fission Network for Semantic Level Fact Verification

Continual learning is one of the key components of human learning and a necessary requirement of artificial intelligence. As dialogue can potentially span infinitely many topics and tasks, a task-oriented dialogue system must have the capability to continually learn, dynamically adapting to new challenges while preserving the knowledge it already acquired. Despite the importance, continual reinforcement learning of the dialogue policy has remained largely unaddressed. The lack of a framework with training protocols, baseline models and suitable metrics, has so far hindered research in this direction. In this work we fill precisely this gap, enabling research in dialogue policy optimisation to go from static to dynamic learning. We provide a continual learning algorithm, baseline architectures and metrics for assessing continual learning models. Moreover, we propose the dynamic dialogue policy transformer (DDPT), a novel dynamic architecture that can integrate new knowledge seamlessly, is capable of handling large state spaces and obtains significant zero-shot performance when being exposed to unseen domains, without any growth in network parameter size. We validate the strengths of DDPT in simulation with two user simulators as well as with humans.

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Uncertainty-aware Propagation Structure Reconstruction for Fake News Detection

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Multi Graph Neural Network for Extractive Long Document Summarization

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