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This paper studies a new problem setting of entity alignment for knowledge graphs (KGs). Since KGs possess different sets of entities, there could be entities that cannot find alignment across them, leading to the problem of dangling entities. As the first attempt to this problem, we construct a new dataset and design a multi-task learning framework for both entity alignment and dangling entity detection. The framework can opt to abstain from predicting alignment for the detected dangling entities. We propose three techniques for dangling entity detection that are based on the distribution of nearest-neighbor distances, i.e., nearest neighbor classification, marginal ranking and background ranking. After detecting and removing dangling entities, an incorporated entity alignment model in our framework can provide more robust alignment for remaining entities. Comprehensive experiments and analyses demonstrate the effectiveness of our framework. We further discover that the dangling entity detection module can, in turn, improve alignment learning and the final performance. The contributed resource is publicly available to foster further research.

IJCNLP-AACL 2021

Knowing the No-match: Entity Alignment with Dangling Cases

dangling entities

entity alignment

knowledge graph

**Welcome to ACL-IJCNLP 2021!**

The great event is jointly organized by the Association for Computational Linguistics (ACL) and Asian Federation of Natural Language Processing (AFNLP). 

As in previous years, the program of the conference includes a poster session, tutorials, workshops and demonstrations in addition to the main conference.


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The great event is jointly organized by the Association for Computational Linguistics (ACL) and Asian Federation of Natural Language Processing (AFNLP).

technical paper

This ability to learn consecutive tasks without forgetting how to perform previously trained problems is essential for developing an online dialogue system. This paper proposes an effective continual learning method for the task-oriented dialogue system with iterative network pruning, expanding, and masking (TPEM), which preserves performance on previously encountered tasks while accelerating learning progress on subsequent tasks. Specifically, TPEM (i) leverages network pruning to keep the knowledge for old tasks, (ii) adopts network expanding to create free weights for new tasks, and (iii) introduces task-specific network masking to alleviate the negative impact of fixed weights of old tasks on new tasks. We conduct extensive experiments on seven different tasks from three benchmark datasets and show empirically that TPEM leads to significantly improved results over the strong competitors.

Continual Learning for Task-oriented Dialogue System with Iterative Network Pruning, Expanding and Masking

Document-level event extraction aims to recognize event information from a whole piece of article.
Existing methods are not effective due to two challenges of this task: a) the target event arguments are scattered across sentences; b) the correlation among events in a document is non-trivial to model.
In this paper, we propose Heterogeneous Graph-based Interaction Model with a Tracker (GIT) to solve the aforementioned two challenges.
For the first challenge, GIT constructs a heterogeneous graph interaction network to capture global interactions among different sentences and entity mentions.
For the second, GIT introduces a Tracker module to track the extracted events and hence capture the interdependency among the events.
Experiments on a large-scale dataset (Zheng et al, 2019) show GIT outperforms the previous methods by 2.8 F1.
Further analysis reveals \modelname is effective in extracting multiple correlated events and event arguments that scatter across the document.

Document-level Event Extraction via Heterogeneous Graph-based Interaction Model with a Tracker

This paper presents a novel method for nested named entity recognition. As a layered method, our method extends the prior second-best path recognition method by explicitly excluding the influence of the best path. Our method maintains a set of hidden states at each time step and selectively leverages them to build a different potential function for recognition at each level. In addition, we demonstrate that recognizing innermost entities first results in better performance than the conventional outermost entities first scheme. We provide extensive experimental results on ACE2004, ACE2005, and GENIA datasets to show the effectiveness and efficiency of our proposed method.

Nested Named Entity Recognition via Explicitly Excluding the Influence of the Best Path

Modern models for event causality identification (ECI) are mainly based on supervised learning, which are prone to the data lacking problem. Unfortunately, the existing NLP-related augmentation methods cannot directly produce available data required for this task. To solve the data lacking problem, we introduce a new approach to augment training data for event causality identification, by iteratively generating new examples and classifying event causality in a dual learning framework. On the one hand, our approach is knowledge guided, which can leverage existing knowledge bases to generate well-formed new sentences. On the other hand, our approach employs a dual mechanism, which is a learnable augmentation framework, and can interactively adjust the generation process to generate task-related sentences. Experimental results on two benchmarks EventStoryLine and Causal-TimeBank show that 1) our method can augment suitable task-related training data for ECI; 2) our method outperforms previous methods on EventStoryLine and Causal-TimeBank (+2.5 and +2.1 points on F1 value respectively).

LearnDA: Learnable Knowledge-Guided Data Augmentation for Event Causality Identification

Distantly supervision automatically generates plenty of training samples for relation extraction. 
However, it also incurs two major problems: noisy labels and imbalanced training data.
Previous works focus more on reducing wrongly labeled relations (false positives) while few explore the missing relations that are caused by incompleteness of knowledge base (false negatives).
Furthermore, the quantity of negative labels overwhelmingly surpasses the positive ones in previous problem formulations.
In this paper, we first provide a thorough analysis of the above challenges caused by negative data.
Next, we formulate the problem of relation extraction into as a positive unlabeled learning task to alleviate false negative problem. 
Thirdly, we propose a pipeline approach, dubbed \textsc{ReRe}, that first performs sentence classification with relational labels and then extracts the subjects/objects.
Experimental results show that the proposed method consistently outperforms existing approaches and remains excellent performance even learned with a large quantity of false positive samples.
Source code is available online at https://github.com/redreamality/RERE-relation-extraction.

Revisiting the Negative Data of Distantly Supervised Relation Extraction

Generative linguistic steganography mainly utilized language models and applied steganographic sampling (stegosampling) to generate high-security steganographic text (stegotext). However, previous methods generally lead to statistical differences between the conditional probability distributions of stegotext and natural text, which brings about security risks. In this paper, to further ensure security, we present a novel provably secure generative linguistic steganographic method ADG, which recursively embeds secret information by Adaptive Dynamic Grouping of tokens according to their probability given by an off-the-shelf language model. We not only prove the security of ADG mathematically, but also conduct extensive experiments on three public corpora to further verify its imperceptibility. The experimental results reveal that the proposed method is able to generate stegotext with nearly perfect security.

Provably Secure Generative Linguistic Steganography

Recently, Text-to-SQL for multi-turn dialogue has attracted great interest. Here, the user input of the current turn is parsed into the corresponding SQL query of the appropriate database, given all previous dialogue history. Current approaches mostly employ end-to-end models and consequently face two challenges. First, dialogue history modeling and Text-to-SQL parsing are implicitly combined, hence it is hard to carry out interpretable analysis and obtain targeted improvement. Second, SQL annotation of multi-turn dialogue is very expensive, leading to training data sparsity. In this paper, we propose a novel decoupled multi-turn Text-to-SQL framework, where an utterance rewrite model first explicitly solves the completion of dialogue context and then a single-turn Text-to-SQL parser follows. A dual learning approach is also proposed for the utterance rewrite model to address the data sparsity problem. Compared with end-to-end approaches, the proposed decoupled method can achieve excellent performance without any annotated in-domain data. With just a few annotated rewrite cases, the decoupled method outperforms the released state-of-the-art end-to-end models on both SParC and CoSQL datasets.

Decoupled Dialogue Modeling and Semantic Parsing for Multi-Turn Text-to-SQL

Relational fact extraction aims to extract semantic triples from unstructured text. In this work, we show that all of the relational fact extraction models can be organized according to a graph-oriented analytical perspective. An efficient model, aD}acency lIst oRiented rElational faCT (DIRECT), is proposed based on this analytical framework. To alleviate challenges of error propagation and sub-task loss equilibrium, DIRECT employs a novel adaptive multi-task learning strategy with dynamic sub-task loss balancing. Extensive experiments are conducted on two benchmark datasets, and results prove that the proposed model outperforms a series of state-of-the-art (SoTA) models for relational triple extraction.

Adjacency List Oriented Relational Fact Extraction via Adaptive Multi-task Learning

We present a novel model for the problem of ranking a collection of documents according to their semantic similarity to a source (query) document. While the problem of document-to-document similarity ranking has been studied, most modern methods are limited to relatively short documents or rely on the existence of "ground-truth'' similarity labels. Yet, in most common real-world cases, similarity ranking is an unsupervised problem as similarity labels are unavailable. Moreover, an ideal model should not be restricted by documents' length. Hence, we introduce SDR, a self-supervised method for document similarity that can be applied to documents of arbitrary length. 
Importantly, SDR can be effectively applied to extremely long documents, exceeding the $4,096$ maximal token limit of Longformer.
Extensive evaluations on large document datasets show that SDR significantly outperforms its alternatives across all metrics. To accelerate future research on unlabeled long document similarity ranking, and as an additional contribution to the community, we herein publish two human-annotated test sets of long documents similarity evaluation. The SDR code and datasets are publicly available.

Self-Supervised Document Similarity Ranking via Contextualized Language Models and Hierarchical Inference

Recent years have seen many breakthroughs in natural language processing (NLP), transitioning it from a mostly theoretical field to one with vast real-world applications. Noting precursor applications in other machine learning and AI techniques with pervasive societal impact, we anticipate the rising importance of developing NLP technologies for social good. Inspired by theories in moral philosophy and global priority research, we aim to promote a future guideline for social good in the context of NLP. We lay the foundations via \textit{moral philosophy}'s definition of social good, and propose a framework to provide a categorization of NLP tasks on the basis of real-world impact, along with metrics to calculate the expected social impact of NLP technology. Based on these fundamental frameworks, we adopt the methodology of global priority research to identify priority causes for NLP research, and use thought experiments to illustrate research priority under hypothetical situations. Finally, we use our theoretical framework to quantify ACL 2020 papers on the basis of societal impact, and provide a checklist for ensuring the creation of NLP technologies that advance social good. Based on these tools, we envision new usage of NLP to effectively improve social good and call attention to important NLP problems with the potential for strong social impact.

Knowing the No-match: Entity Alignment with Dangling Cases

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Knowing the No-match: Entity Alignment with Dangling Cases

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