Korea, Republic of

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.

COLING 2022

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

sentient analysis

multimodal

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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.

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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.

technical paper

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.

Towards Robust Neural Machine Translation with Iterative Scheduled Data-Switch Training

We examine the link between facets of Rhetorical Structure Theory (RST) and the selection of content for extractive summarisation, for German-language texts. For this purpose, we produce a set of extractive summaries for a dataset of German-language newspaper commentaries, a corpus which already has several layers of annotation. We provide an in-depth analysis of the connection between summary sentences and several RST-based features and transfer these insights to various automated summarisation models. Our results show that RST features are informative for the task of extractive summarisation, particularly nuclearity and relations at sentence-level.

Extractive summarisation for German-language data: a text-level approach with discourse features

As a fundamental natural language processing task and one of core knowledge extraction techniques, named entity recognition (NER) is widely used to extract information from texts for downstream tasks. Nested NER is a branch of NER in which the named entities (NEs) are nested with each other. However, most of the previous studies on nested NER usually apply linear structure to model the nested NEs which are actually accommodated in a hierarchical structure. Thus in order to address this mismatch, this work models the full nested NEs in a sentence as a holistic structure, then we propose a holistic structure parsing algorithm to disclose the entire NEs once for all. Besides, there is no research on applying corpus level information to NER currently. To make up for the loss of this information, we introduce Point-wise Mutual Information (PMI) and other frequency features from corpus-aware statistics for even better performance by holistic modeling from sentence-level to corpuslevel. Experiments show that our model yields promising results on widely-used benchmarks which approach or even achieve state-of-theart. Further empirical studies show that our proposed corpus-aware features can substantially improve NER domain adaptation, which demonstrates the surprising advantage of our proposed corpus-level holistic structure modeling.

Nested Named Entity Recognition as Corpus Aware Holistic Structure Parsing

Existing studies typically handle aspect-based sentiment analysis by stacking multiple neural modules, which inevitably result in severe error propagation. Instead, we propose a novel endto-end framework, MRCOOL: MRC-PrOmptmOdeL framework, where numerous sentiment aspects are elicited by a machine reading comprehension (MRC) model and their corresponding sentiment polarities are classified in a prompt learning way. Experiments show that our end-to-end framework consistently yields promising results on widely-used benchmark datasets which significantly outperform existing state-of-the-art models or achieve comparable performance.

Aspect-based Sentiment Analysis as Machine Reading Comprehension

Automatic extraction of funding information from academic articles adds significant value to industry and research communities, including tracking research outcomes by funding organizations, profiling researchers and universities based on the received funding, and supporting open access policies. Two major challenges of identifying and linking funding entities are: (i) sparse graph structure of the Knowledge Base (KB), which makes the commonly used graph-based entity linking approaches suboptimal for the funding domain, (ii) missing entities in KB, which (unlike recent zero-shot approaches) requires marking entity mentions without KB entries as NIL. We propose an entity linking model that can perform NIL prediction and overcome data scarcity issues in a time and data-efficient manner. Our model builds on a transformer-based mention detection and a bi-encoder model to perform entity linking. We show that our model outperforms strong existing baselines.

Find the Funding: Entity Linking with Incomplete Funding Knowledge Bases

The task of generating texts of different categories has attracted more and more attention in the area of natural language generation recently. Meanwhile, generative adversarial net (GAN) has demonstrated its effectiveness on text generation, and is further applied to category text generation in later works. Different from existing methods, which mainly consider the pairwise relations between the text embedding and the corresponding fixed one-hot class label (data-to-class relations), this paper proposes a novel Contrastive Category Generative Adversarial Net (CoCGAN) to incorporate contrastive learning into adversarial category text generation, considering more flexible data-to-class relations as well as relations between the multiple text embeddings in the same batch (data-to-data relations). The discriminator of CoCGAN discriminates the authenticity of given samples and optimizes a contrastive learning objective to capture both more flexible data-to-class relations and data-to-data relations among training samples. Accordingly, the generator tries to produce more realistic samples which can confuse the discriminator. Experimental results on both synthetic and real category text generation datasets demonstrate that CoCGAN can achieve significant improvements over the baseline category text generation models.

CoCGAN: Contrastive Learning for Adversarial Category Text Generation

Automated radiology report generation aims to generate paragraphs that describe fine-grained visual differences among cases, especially those between the normal and the diseased. Existing methods seldom consider the cross-modal alignment between textual and visual features and tend to ignore disease tags as an auxiliary for report generation. To bridge the gap between textual and visual information, in this study, we propose a "Jointly learning framework for automated disease Prediction and radiology report Generation (JPG)" to improve the quality of reports through the interaction between the main task (report generation) and two auxiliary tasks (feature alignment and disease prediction). The feature alignment and disease prediction help the model learn text-correlated visual features and record diseases as keywords so that it can output high-quality reports. Besides, the improved reports in turn provide additional harder samples for feature alignment and disease prediction to learn more precise visual and textual representations and improve prediction accuracy. All components are jointly trained in a manner that helps improve them iteratively and progressively. Experimental results demonstrate the effectiveness of JPG on the most commonly used IU X-RAY dataset, showing its superior performance over multiple state-of-the-art image captioning and medical report generation methods with regard to BLEU, METEOR, and ROUGE metrics.

JPG - Jointly Learn to Align: Automated Disease Prediction and Radiology Report Generation

This article presents the specification and evaluation of DiaBiz.Kom – the corpus of dialogue texts in Polish. The corpus contains transcriptions of telephone conversations conducted according to a prepared scenario. The transcripts of conversations have been manually annotated with a layer of information concerning communicative functions. DiaBiz.Kom is the first corpus of this type prepared for the Polish language and will be used to develop a system of dialogue analysis and modules for creating advanced chatbots.

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

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

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