pwc-archive/papers-with-abstracts huggingface dataset:
Language-Independent Approach for Morphological Disambiguation
Authors: Alymzhan Toleu, Gulmira Tolegen, Rustam Mussabayev
Date: 2022-10-01 00:00:00
Proceeding: COLING 2022 10
Tasks: Morphological Analysis, Morphological Disambiguation, POS, POS Tagging

This paper presents a language-independent approach for morphological disambiguation which has been regarded as an extension of POS tagging, jointly predicting complex morphological tags. In the proposed approach, all words, roots, POS and morpheme tags are embedded into vectors, and contexts representations from surface word and morphological contexts are calculated. Then the inner products between analyses and the context’s representations are computed to perform the disambiguation. The underlying hypothesis is that the correct morphological analysis should be closer to the context in a vector space. Experimental results show that the proposed approach outperforms the existing models on seven different language datasets. Concretely, compared with the baselines of MarMot and a sophisticated neural model (Seq2Seq), the proposed approach achieves around 6% improvement in average accuracy for all languages while running about 6 and 33 times faster than MarMot and Seq2Seq, respectively.
Deciphering and Characterizing Out-of-Vocabulary Words for Morphologically Rich Languages
Authors: Georgie Botev, Arya D. McCarthy, Winston Wu, David Yarowsky
Date: 2022-10-01 00:00:00
Proceeding: COLING 2022 10
Tasks: Language Modeling, Language Modelling, Machine Translation, Translation

This paper presents a detailed foundational empirical case study of the nature of out-of-vocabulary words encountered in modern text in a moderate-resource language such as Bulgarian, and a multi-faceted distributional analysis of the underlying word-formation processes that can aid in their compositional translation, tagging, parsing, language modeling, and other NLP tasks. Given that out-of-vocabulary (OOV) words generally present a key open challenge to NLP and machine translation systems, especially toward the lower limit of resource availability, there are useful practical insights, as well as corpus-linguistic insights, from both a detailed manual and automatic taxonomic analysis of the types, multidimensional properties, and processing potential for multiple representative OOV data samples.
String Editing Based Chinese Grammatical Error Diagnosis
Authors: Haihua Xie, Xiaoqing Lyu, Xuefei Chen
Date: 2022-10-01 00:00:00
Proceeding: COLING 2022 10
Tasks: TAG

Chinese Grammatical Error Diagnosis (CGED) suffers the problems of numerous types of grammatical errors and insufficiency of training data. In this paper, we propose a string editing based CGED model that requires less training data by using a unified workflow to handle various types of grammatical errors. Two measures are proposed in our model to enhance the performance of CGED. First, the detection and correction of grammatical errors are divided into different stages. In the stage of error detection, the model only outputs the types of grammatical errors so that the tag vocabulary size is significantly reduced compared with other string editing based models. Secondly, the correction of some grammatical errors is converted to the task of masked character inference, which has plenty of training data and mature solutions. Experiments on datasets of NLPTEA-CGED demonstrate that our model outperforms other CGED models in many aspects.
Speaker-Aware Discourse Parsing on Multi-Party Dialogues
Authors: Nan Yu, Guohong Fu, Min Zhang
Date: 2022-10-01 00:00:00
Proceeding: COLING 2022 10
Tasks: Discourse Parsing

Discourse parsing on multi-party dialogues is an important but difficult task in dialogue systems and conversational analysis. It is believed that speaker interactions are helpful for this task. However, most previous research ignores speaker interactions between different speakers. To this end, we present a speaker-aware model for this task. Concretely, we propose a speaker-context interaction joint encoding (SCIJE) approach, using the interaction features between different speakers. In addition, we propose a second-stage pre-training task, same speaker prediction (SSP), enhancing the conversational context representations by predicting whether two utterances are from the same speaker. Experiments on two standard benchmark datasets show that the proposed model achieves the best-reported performance in the literature. We will release the codes of this paper to facilitate future research.
Iterative Span Selection: Self-Emergence of Resolving Orders in Semantic Role Labeling
Authors: Shuhei Kurita, Hiroki Ouchi, Kentaro Inui, Satoshi Sekine
Date: 2022-10-01 00:00:00
Proceeding: COLING 2022 10
Tasks: Semantic Role Labeling

Semantic Role Labeling (SRL) is the task of labeling semantic arguments for marked semantic predicates. Semantic arguments and their predicates are related in various distinct manners, of which certain semantic arguments are a necessity while others serve as an auxiliary to their predicates. To consider such roles and relations of the arguments in the labeling order, we introduce iterative argument identification (IAI), which combines global decoding and iterative identification for the semantic arguments. In experiments, we first realize that the model with random argument labeling orders outperforms other heuristic orders such as the conventional left-to-right labeling order. Combined with simple reinforcement learning, the proposed model spontaneously learns the optimized labeling orders that are different from existing heuristic orders. The proposed model with the IAI algorithm achieves competitive or outperforming results from the existing models in the standard benchmark datasets of span-based SRL: CoNLL-2005 and CoNLL-2012.
Position Offset Label Prediction for Grammatical Error Correction
Authors: Xiuyu Wu, Jingsong Yu, Xu sun, Yunfang Wu
Date: 2022-10-01 00:00:00
Proceeding: COLING 2022 10
Tasks: Data Augmentation, Decoder, Grammatical Error Correction, Position, Prediction, Sentence

We introduce a novel position offset label prediction subtask to the encoder-decoder architecture for grammatical error correction (GEC) task. To keep the meaning of the input sentence unchanged, only a few words should be inserted or deleted during correction, and most of tokens in the erroneous sentence appear in the paired correct sentence with limited position movement. Inspired by this observation, we design an auxiliary task to predict position offset label (POL) of tokens, which is naturally capable of integrating different correction editing operations into a unified framework. Based on the predicted POL, we further propose a new copy mechanism (P-copy) to replace the vanilla copy module. Experimental results on Chinese, English and Japanese datasets demonstrate that our proposed POL-Pc framework obviously improves the performance of baseline models. Moreover, our model yields consistent performance gain over various data augmentation methods. Especially, after incorporating synthetic data, our model achieves a 38.95 F-0.5 score on Chinese GEC dataset, which outperforms the previous state-of-the-art by a wide margin of 1.98 points.
Parsing Natural Language into Propositional and First-Order Logic with Dual Reinforcement Learning
Authors: Xuantao Lu, Jingping Liu, Zhouhong Gu, Hanwen Tong, Chenhao Xie, Junyang Huang, Yanghua Xiao, Wenguang Wang
Date: 2022-10-01 00:00:00
Proceeding: COLING 2022 10
Tasks: Natural Language Inference, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Semantic Parsing

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 competitors on several datasets. Furthermore, by introducing PL/FOL generated by our model, the performance of existing Natural Language Inference (NLI) models is further enhanced.
Enhancing Structure-aware Encoder with Extremely Limited Data for Graph-based Dependency Parsing
Authors: Yuanhe Tian, Yan Song, Fei Xia
Date: 2022-10-01 00:00:00
Proceeding: COLING 2022 10
Tasks: 2k, Dependency Parsing, Sentence

Dependency parsing is an important fundamental natural language processing task which analyzes the syntactic structure of an input sentence by illustrating the syntactic relations between words. To improve dependency parsing, leveraging existing dependency parsers and extra data (e.g., through semi-supervised learning) has been demonstrated to be effective, even though the final parsers are trained on inaccurate (but massive) data. In this paper, we propose a frustratingly easy approach to improve graph-based dependency parsing, where a structure-aware encoder is pre-trained on auto-parsed data by predicting the word dependencies and then fine-tuned on gold dependency trees, which differs from the usual pre-training process that aims to predict the context words along dependency paths. Experimental results and analyses demonstrate the effectiveness and robustness of our approach to benefit from the data (even with noise) processed by different parsers, where our approach outperforms strong baselines under different settings with different dependency standards and model architectures used in pre-training and fine-tuning. More importantly, further analyses find that only 2K auto-parsed sentences are required to obtain improvement when pre-training vanilla BERT-large based parser without requiring extra parameters.
Simple and Effective Graph-to-Graph Annotation Conversion
Authors: Yuxuan Wang, Zhilin Lei, Yuqiu Ji, Wanxiang Che
Date: 2022-10-01 00:00:00
Proceeding: COLING 2022 10

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 score. 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 score than those generated by the baselines.
BiBL: AMR Parsing and Generation with Bidirectional Bayesian Learning
Authors: Ziming Cheng, Zuchao Li, Hai Zhao
Date: 2022-10-01 00:00:00
Proceeding: COLING 2022 10
Tasks: Abstract Meaning Representation, AMR Parsing, AMR-to-Text Generation, Text Generation

Abstract Meaning Representation (AMR) offers a unified semantic representation for natural language sentences. Thus transformation between AMR and text yields two transition tasks in opposite directions, i.e., Text-to-AMR parsing and AMR-to-Text generation. Existing AMR studies only focus on one-side improvements despite the duality of the two tasks, and their improvements are greatly attributed to the inclusion of large extra training data or complex structure modifications which harm the inference speed. Instead, we propose data-efficient Bidirectional Bayesian learning (BiBL) to facilitate bidirectional information transition by adopting a single-stage multitasking strategy so that the resulting model may enjoy much lighter training at the same time. Evaluation on benchmark datasets shows that our proposed BiBL outperforms strong previous seq2seq refinements without the help of extra data which is indispensable in existing counterpart models. We release the codes of BiBL at: https://github.com/KHAKhazeus/BiBL.
Multi-Layer Pseudo-Siamese Biaffine Model for Dependency Parsing
Authors: Ziyao Xu, Houfeng Wang, Bingdong Wang
Date: 2022-10-01 00:00:00
Proceeding: COLING 2022 10
Tasks: ARC, Dependency Parsing, Form

Biaffine method is a strong and efficient method for graph-based dependency parsing. However, previous work only used the biaffine method at the end of the dependency parser as a scorer, and its application in multi-layer form is ignored. In this paper, we propose a multi-layer pseudo-Siamese biaffine model for neural dependency parsing. In this model, we modify the biaffine method so that it can be utilized in multi-layer form, and use pseudo-Siamese biaffine module to construct arc weight matrix for final prediction. In our proposed multi-layer architecture, the biaffine method plays important roles in both scorer and attention mechanism at the same time in each layer. We evaluate our model on PTB, CTB, and UD. The model achieves state-of-the-art results on these datasets. Further experiments show the benefits of introducing multi-layer form and pseudo-Siamese module into the biaffine method with low efficiency loss.
Towards Understanding the Relation between Gestures and Language
Authors: Artem Abzaliev, Andrew Owens, Rada Mihalcea
Date: 2022-10-01 00:00:00
Proceeding: COLING 2022 10
Tasks: Relation

In this paper, we explore the relation between gestures and language. Using a multimodal dataset, consisting of Ted talks where the language is aligned with the gestures made by the speakers, we adapt a semi-supervised multimodal model to learn gesture embeddings. We show that gestures are predictive of the native language of the speaker, and that gesture embeddings further improve language prediction result. In addition, gesture embeddings might contain some linguistic information, as we show by probing embeddings for psycholinguistic categories. Finally, we analyze the words that lead to the most expressive gestures and find that function words drive the expressiveness of gestures.
Building Joint Relationship Attention Network for Image-Text Generation
Authors: Changzhi Wang, Xiaodong Gu
Date: 2022-10-01 00:00:00
Proceeding: COLING 2022 10
Tasks: Text Generation

Attention based methods for image-text generation often focus on visual features individually, while ignoring relationship information among image features that provides important guidance for generating sentences. To alleviate this issue, in this work we propose the Joint Relationship Attention Network (JRAN) that novelly explores the relationships among the features. Specifically, different from the previous relationship based approaches that only explore the single relationship in the image, our JRAN can effectively learn two relationships, the visual relationships among region features and the visual-semantic relationships between region features and semantic features, and further make a dynamic trade-off between them during outputting the relationship representation. Moreover, we devise a new relationship based attention, which can adaptively focus on the output relationship representation when predicting different words. Extensive experiments on large-scale MSCOCO and small-scale Flickr30k datasets show that JRAN achieves state-of-the-art performance. More remarkably, JRAN achieves new 28.3% and 58.2% performance in terms of BLEU4 and CIDEr metric on Flickr30k dataset.
Learning to Focus on the Foreground for Temporal Sentence Grounding
Authors: Daizong Liu, Wei Hu
Date: 2022-10-01 00:00:00
Proceeding: COLING 2022 10
Tasks: Sentence, Temporal Sentence Grounding, Video Understanding

Temporal sentence grounding (TSG) is crucial and fundamental for video understanding. Previous works typically model the target activity referred to the sentence query in a video by extracting the appearance information from each whole frame. However, these methods fail to distinguish visually similar background noise and capture subtle details of small objects. Although a few recent works additionally adopt a detection model to filter out the background contents and capture local appearances of foreground objects, they rely on the quality of the detection model and suffer from the time-consuming detection process. To this end, we propose a novel detection-free framework for TSG—Grounding with Learnable Foreground (GLF), which efficiently learns to locate the foreground regions related to the query in consecutive frames for better modelling the target activity. Specifically, we first split each video frame into multiple patch candidates of equal size, and reformulate the foreground detection problem as a patch localization task. Then, we develop a self-supervised coarse-to-fine paradigm to learn to locate the most query-relevant patch in each frame and aggregate them among the video for final grounding. Further, we employ a multi-scale patch reasoning strategy to capture more fine-grained foreground information. Extensive experiments on three challenging datasets (Charades-STA, TACoS, ActivityNet) show that the proposed GLF outperforms state-of-the-art methods.
Are Visual-Linguistic Models Commonsense Knowledge Bases?
Authors: Hsiu-Yu Yang, Carina Silberer
Date: 2022-10-01 00:00:00
Proceeding: COLING 2022 10
Tasks: Natural Language Understanding, Question Answering

Despite the recent success of pretrained language models as on-the-fly knowledge sources for various downstream tasks, they are shown to inadequately represent trivial common facts that vision typically captures. This limits their application to natural language understanding tasks that require commonsense knowledge. We seek to determine the capability of pretrained visual-linguistic models as knowledge sources on demand. To this end, we systematically compare language-only and visual-linguistic models in a zero-shot commonsense question answering inference task. We find that visual-linguistic models are highly promising regarding their benefit for text-only tasks on certain types of commonsense knowledge associated with the visual world. Surprisingly, this knowledge can be activated even when no visual input is given during inference, suggesting an effective multimodal fusion during pretraining. However, we reveal that there is still a huge space for improvement towards better cross-modal reasoning abilities and pretraining strategies for event understanding.
Visual Prompt Tuning for Few-Shot Text Classification
Authors: Jingyuan Wen, Yutian Luo, Nanyi Fei, Guoxing Yang, Zhiwu Lu, Hao Jiang, Jie Jiang, Zhao Cao
Date: 2022-10-01 00:00:00
Proceeding: COLING 2022 10
Tasks: Classification, Few-Shot Learning, Few-Shot Text Classification, image-classification, Image Classification, text-classification, Text Classification, Visual Prompt Tuning

Deploying large-scale pre-trained models in the prompt-tuning paradigm has demonstrated promising performance in few-shot learning. Particularly, vision-language pre-training models (VL-PTMs) have been intensively explored in various few-shot downstream tasks. However, most existing works only apply VL-PTMs to visual tasks like image classification, with few attempts being made on language tasks like text classification. In few-shot text classification, a feasible paradigm for deploying VL-PTMs is to align the input samples and their category names via the text encoders. However, it leads to the waste of visual information learned by the image encoders of VL-PTMs. To overcome this drawback, we propose a novel method named Visual Prompt Tuning (VPT). To our best knowledge, this method is the first attempt to deploy VL-PTM in few-shot text classification task. The main idea is to generate the image embeddings w.r.t. category names as visual prompt and then add them to the aligning process. Extensive experiments show that our VPT can achieve significant improvements under both zero-shot and few-shot settings. Importantly, our VPT even outperforms the most recent prompt-tuning methods on five public text classification datasets.
Systematic Analysis of Image Schemas in Natural Language through Explainable Multilingual Neural Language Processing
Authors: Lennart Wachowiak, Dagmar Gromann
Date: 2022-10-01 00:00:00
Proceeding: COLING 2022 10
Tasks: Sentence

In embodied cognition, physical experiences are believed to shape abstract cognition, such as natural language and reasoning. Image schemas were introduced as spatio-temporal cognitive building blocks that capture these recurring sensorimotor experiences. The few existing approaches for automatic detection of image schemas in natural language rely on specific assumptions about word classes as indicators of spatio-temporal events. Furthermore, the lack of sufficiently large, annotated datasets makes evaluation and supervised learning difficult. We propose to build on the recent success of large multilingual pretrained language models and a small dataset of examples from image schema literature to train a supervised classifier that classifies natural language expressions of varying lengths into image schemas. Despite most of the training data being in English with few examples for German, the model performs best in German. Additionally, we analyse the model’s zero-shot performance in Russian, French, and Mandarin. To further investigate the model’s behaviour, we utilize local linear approximations for prediction probabilities that indicate which words in a sentence the model relies on for its final classification decision. Code and dataset are publicly available.
ACT-Thor: A Controlled Benchmark for Embodied Action Understanding in Simulated Environments
Authors: Michael Hanna, Federico Pedeni, Alessandro Suglia, Alberto Testoni, Raffaella Bernardi
Date: 2022-10-01 00:00:00
Proceeding: COLING 2022 10
Tasks: Action Understanding

Artificial agents are nowadays challenged to perform embodied AI tasks. To succeed, agents must understand the meaning of verbs and how their corresponding actions transform the surrounding world. In this work, we propose ACT-Thor, a novel controlled benchmark for embodied action understanding. We use the AI2-THOR simulated environment to produce a controlled setup in which an agent, given a before-image and an associated action command, has to determine what the correct after-image is among a set of possible candidates. First, we assess the feasibility of the task via a human evaluation that resulted in 81.4% accuracy, and very high inter-annotator agreement (84.9%). Second, we design both unimodal and multimodal baselines, using state-of-the-art visual feature extractors. Our evaluation and error analysis suggest that only models that have a very structured representation of the actions together with powerful visual features can perform well on the task. However, they still fall behind human performance in a zero-shot scenario where the model is exposed to unseen (action, object) pairs. This paves the way for a systematic way of evaluating embodied AI agents that understand grounded actions.