Korea, Republic of

Knowledge representation learning is a key step required for link prediction tasks with knowledge graphs (KGs).
During the learning process, the semantics of each entity is embedded by a vector or a point in a feature space.
The distance between these points is a measure of semantic similarity.
However, in a KG, while two entities may have similar semantics in some relations, they have different semantics in others.
It is ambiguous to assign a fixed distance to depict the variant semantic similarity of entities.
To alleviate the semantic ambiguity in KGs, 
we design a new embedding approach named OpticE, which is derived from the well-known physical phenomenon of optical interference.
It is a lightweight and relation-adaptive model based on coherence theory, in which each entity&#39;s semantics vary automatically regarding different relations.
In addition, a unique negative sampling method is proposed to combine the multimapping properties and self-adversarial learning during the training process.
The experimental results obtained on practical KG benchmarks show that the OpticE model, with elegant structures, can compete with existing link prediction methods.

COLING 2022

OpticE: A Coherence Theory-Based Model for Link Prediction

embedding model

link prediction

knowledge graph

ECAI2020 featured a full programme of technical papers, as well as many AI-related events of interest for researchers, students and all attendants who are interested on contemporary AI, including:




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**The EU Challenges forum**

… and many more!

ECAI 2020

ECAI 2020 featured a full programme of technical papers, as well as many AI-related events of interest for researchers, students and all attendants who are interested on contemporary AI.

Knowledge representation learning is a key step required for link prediction tasks with knowledge graphs (KGs).
During the learning process, the semantics of each entity is embedded by a vector or a point in a feature space.
The distance between these points is a measure of semantic similarity.
However, in a KG, while two entities may have similar semantics in some relations, they have different semantics in others.
It is ambiguous to assign a fixed distance to depict the variant semantic similarity of entities.
To alleviate the semantic ambiguity in KGs, 
we design a new embedding approach named OpticE, which is derived from the well-known physical phenomenon of optical interference.
It is a lightweight and relation-adaptive model based on coherence theory, in which each entity's semantics vary automatically regarding different relations.
In addition, a unique negative sampling method is proposed to combine the multimapping properties and self-adversarial learning during the training process.
The experimental results obtained on practical KG benchmarks show that the OpticE model, with elegant structures, can compete with existing link prediction methods.

poster

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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.

In the real-world question answering scenarios, hybrid form combining both tabular and textual contents has attracted more and more attention, among which numerical reasoning problem is one of the most typical and challenging problems. Existing methods usually adopt encoder-decoder framework to represent hybrid contents and generate answers. However, it can not capture the rich relationship among numerical value, table schema, and text information on the encoder side. The decoder uses a simple predefined operator classifier which is not flexible enough to handle numerical reasoning processes with diverse expressions. To address these problems, this paper proposes a \textbf{Re}lational \textbf{G}raph enhanced \textbf{H}ybrid table-text \textbf{N}umerical reasoning model with \textbf{T}ree decoder (\textbf{RegHNT}). It models the numerical question answering over table-text hybrid contents as an expression tree generation task. Moreover, we propose a novel relational graph modeling method, which models alignment between questions, tables, and paragraphs. We validated our model on the publicly available table-text hybrid QA benchmark (TAT-QA). The proposed RegHNT significantly outperform the baseline model and achieve state-of-the-art results\footnote{We openly released the source code and data at~\url{https://github.com/lfy79001/RegHNT}}~(2022-05-05).

Answering Numerical Reasoning Questions in Table-Text Hybrid Contents with Graph-based Encoder and Tree-based Decoder

Automatic evaluation of open-domain dialogs remains an unsolved problem. Moreover, existing methods do not correlate strongly with human annotations.
This paper presents a new automated evaluation method using follow-ups: we measure the probability that a language model will continue the conversation with a fixed set of follow-ups (e.g. Not really relevant here, What are you trying to say?). 
When compared against twelve existing methods, our new evaluation achieves the highest correlation with human evaluations.

Open-Domain Dialog Evaluation using Follow-Ups Likelihood

Semantic parsing converts natural language utterances into structured logical expressions. We consider two such formal representations: Propositional Logic (PL) and First-order Logic (FOL). The paucity of labeled data is a major challenge in this field. In previous works, dual reinforcement learning has been proposed as an approach to reduce dependence on labeled data. However, this method has the following limitations: 1) The reward needs to be set manually and is not applicable to all kinds of logical expressions. 2) The training process easily collapses when models are trained with only the reward from dual reinforcement learning. In this paper, we propose a scoring model to automatically learn a model-based reward, and an effective training strategy based on curriculum learning is further proposed to stabilize the training process. In addition to the technical contribution, a Chinese-PL/FOL dataset is constructed to compensate for the paucity of labeled data in this field. Experimental results show that the proposed method outperforms ompetitors on several datasets. Furthermore, by introducing PL/FOL generated by our model, the performance of existing Natural Language Inference (NLI) models is further enhanced.

Parsing Natural Language into Propositional and First-Order Logic with Dual Reinforcement Learning

Domain-specific documents cover terminologies and specialized knowledge. This has been the main challenge of domain-specific document retrieval systems. Previous approaches propose domain-adaptation and transfer learning methods to alleviate this problem. However, these approaches still follow the same document representation method in previous approaches; a document is embedded into a single vector. In this study, we propose VKGDR. VKGDR represents a given corpus into a graph of entities and their relations (known as a virtual knowledge graph) and computes the relevance between queries and documents based on the graph representation. We conduct three experiments 1) domain-specific document retrieval, 2) comparison of our virtual knowledge graph construction method with previous approaches, and 3) ablation study on each component of our virtual knowledge graph. From the results, we see that unsupervised VKGDR outperforms baselines in a zero-shot setting and even outperforms fully-supervised bi-encoder. We also verify that our virtual knowledge graph construction method results in better retrieval performance than previous approaches.

Virtual Knowledge Graph Construction for Zero-Shot Domain-Specific Document Retrieval

The world of pronouns is changing -- from a closed word class with few members to an open set of terms to reflect identities. However, Natural Language Processing (NLP) barely reflects this linguistic shift, resulting in the possible exclusion of non-binary users, even though recent work outlined the harms of gender-exclusive language technology. The current modeling of 3rd person pronouns is particularly problematic. It largely ignores various phenomena like neopronouns, i.e., novel pronoun sets that are not (yet) widely established. This omission contributes to the discrimination of marginalized and underrepresented groups, e.g., non-binary individuals. It thus prevents gender equality, one of the UN's sustainable development goals (goal 5). Further, other identity-expressions beyond gender are ignored by current NLP technology. 
This paper provides an overview of 3rd person pronoun issues for NLP. Based on our observations and ethical considerations, we define a series of five desiderata for modeling pronouns in language technology, which we validate through a survey. We evaluate existing and novel modeling approaches w.r.t. these desiderata qualitatively and quantify the impact of a more discrimination-free approach on an established benchmark dataset.

Welcome to the Modern World of Pronouns: Identity-Inclusive Natural Language Processing beyond Gender

A math word problem (MWP) is a coherent narrative which reflects the underlying logic of math equations. Successful MWP generation can automate the writing of mathematics questions. Previous methods mainly generate MWP text based on inflexible pre-defined templates. In this paper, we propose a neural model for generating MWP text from math equations. Firstly, we incorporate a matching model conditioned on the domain knowledge to retrieve a MWP instance which is most consistent with the ground-truth, where the domain is a latent variable extracted with a domain summarizer. Secondly, by constructing a Quantity Cell Graph (QCG) from the retrieved MWP instance and reasoning over it, we improve the model's comprehension of real-world scenarios and derive a domain-constrained instance sketch to guide the generation. Besides, the QCG also interacts with the equation encoder to enhance the alignment between math tokens (e.g., quantities and variables) and MWP text. Experiments and empirical analysis on educational MWP set show that our model achieves impressive performance in both automatic evaluation metrics and human evaluation metrics.

DISK: Domain-constrained Instance Sketch for Math Word Problem Generation

In recent years, multimodal sentiment analysis (MSA) has attracted more and more interest, which aims to predict the sentiment polarity expressed in a video. Existing methods typically 1) treat three modal features (textual, acoustic, visual) equally, without distinguishing the importance of different modalities; and 2) split the video into frames, leading to missing the global acoustic information. In this paper, we propose a global Acoustic feature enhanced Modal-Order-Aware network (AMOA) to address these problems. Firstly, a modal-order-aware network is designed to obtain the multimodal fusion feature. This network integrates the three modalities in a certain order, which makes the modality at the core position matter more. Then, we introduce the global acoustic feature of the whole video into our model. Since the global acoustic feature and multimodal fusion feature originally reside in their own spaces, contrastive learning is further employed to align them before concatenation. Experiments on two public datasets show that our model outperforms the state-of-the-art models. In addition, we also generalize our model to the sentiment with more complex semantics, such as sarcasm detection. Our model also achieves state-of-the-art performance on a widely used sarcasm dataset.

AMOA: Global Acoustic Feature Enhanced Modal-Order-Aware Network for Multimodal Sentiment Analysis

Automatic math problem solving has attracted much attention of NLP researchers
recently. However, most of the works focus on the solving of Math Word Problems
(MWPs). In this paper, we study on the Geometric Problem Solving based on neural
networks. Solving geometric problems requires the integration of text and diagram
information as well as the knowledge of the relevant theorems. The lack of hig-quality datasets and efficient neural geometric solvers impedes the development of
automatic geometric problems solving. Based on GeoQA, we newly annotate 2,518
geometric problems with richer types and greater difficulty to form an augmented
benchmark dataset GeoQA+1, containing 6,027 problems in training set and 7,528
totally. We further perform data augmentation method to expand the training set to
12,054. Besides, we design a Dual Parallel text Encoder (DPE) to efficiently encode
long and medium-length problem text. The experimental results validate the
effectiveness of GeoQA+ and DPE module, and the accuracy of automatic geometric
problem solving is improved to 66.09%.

An Augmented Benchmark Dataset for Geometric Question Answering through Dual Parallel Text Encoding

Generating temporally-ordered event sequences in texts is important to natural
language processing. Two emerging tasks in this direction are temporal event ordering (rearranging the set of events to correct order) and event infilling (generating an event at a specified position). To tackle the two related tasks, the existing method adopts a vanilla sequence-to-sequence model via maximum
likelihood estimation (MLE). However, applying this approach to these tasks will
cause two issues. One issue is that the MLE loss emphasizes strict local alignment and ignores the global semantics of the event. The other issue is that the model adopts a word-level objective to model events in texts, failing to evaluate the predicted results of the model from the perspective of event sequence. To alleviate these issues, we present a novel model to tackle the generation of temporally-ordered event sequences via Event Optimal Transport (EOT). First, we treat the
events in the sequence as modeling units and explicitly extract the semantics of the events. Second, to provide event sequence-level evaluation of the predicted results of the model, we directly match events in sequences. Extensive experimental results show that our approach outperforms previous models on all evaluation datasets. In particular, the accuracy is improved by 7.7%, and the Macro F1 is improved by 7.2% on one of the datasets.

Generating Temporally-ordered Event Sequences via Event Optimal Transport

Most existing methods on robust neural machine translation (NMT) construct adversarial examples by injecting noise into authentic examples and indiscriminately exploit two types of examples. They require the model to translate both the authentic source sentence and its adversarial counterpart into the identical target sentence within the same training stage, which may be a suboptimal choice to achieve robust NMT. In this paper, we first conduct a preliminary study to confirm this claim and further propose an Iterative Scheduled Data-switch Training Framework to mitigate this problem. Specifically, we introduce two training stages, iteratively switching between authentic and adversarial examples. Compared with previous studies, our model focuses more on just one type of examples at each single stage, which can better exploit authentic and adversarial examples, and thus obtaining a better robust NMT model. Moreover, we introduce an improved curriculum learning method with a sampling strategy to better schedule the process of noise injection. Experimental results show that our model significantly surpasses several competitive baselines on four translation benchmarks.

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OpticE: A Coherence Theory-Based Model for Link Prediction

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Answering Numerical Reasoning Questions in Table-Text Hybrid Contents with Graph-based Encoder and Tree-based Decoder

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