pwc-archive/papers-with-abstracts huggingface dataset:
Syntactic Graph Convolutional Network for Spoken Language Understanding
Authors: Keqing He, Shuyu Lei, Yushu Yang, Huixing Jiang, Zhongyuan Wang
Date: 2020-12-01 00:00:00
Proceeding: COLING 2020 8
Tasks: Intent Detection, Multi-Task Learning, slot-filling, Slot Filling, Spoken Language Understanding

Slot filling and intent detection are two major tasks for spoken language understanding. In most existing work, these two tasks are built as joint models with multi-task learning with no consideration of prior linguistic knowledge. In this paper, we propose a novel joint model that applies a graph convolutional network over dependency trees to integrate the syntactic structure for learning slot filling and intent detection jointly. Experimental results show that our proposed model achieves state-of-the-art performance on two public benchmark datasets and outperforms existing work. At last, we apply the BERT model to further improve the performance on both slot filling and intent detection.
Robust Machine Reading Comprehension by Learning Soft labels
Authors: Zhenyu Zhao, Shuangzhi Wu, Muyun Yang, Kehai Chen, Tiejun Zhao
Date: 2020-12-01 00:00:00
Proceeding: COLING 2020 8
Tasks: Machine Reading Comprehension, Reading Comprehension

Neural models have achieved great success on the task of machine reading comprehension (MRC), which are typically trained on hard labels. We argue that hard labels limit the model capability on generalization due to the label sparseness problem. In this paper, we propose a robust training method for MRC models to address this problem. Our method consists of three strategies, 1) label smoothing, 2) word overlapping, 3) distribution prediction. All of them help to train models on soft labels. We validate our approach on the representative architecture - ALBERT. Experimental results show that our method can greatly boost the baseline with 1{\%} improvement in average, and achieve state-of-the-art performance on NewsQA and QUOREF.
``Judge me by my size (noun), do you?'' YodaLib: A Demographic-Aware Humor Generation Framework
Authors: Aparna Garimella, Carmen Banea, Nabil Hossain, Rada Mihalcea
Date: 2020-12-01 00:00:00
Proceeding: COLING 2020 8
Tasks: Sentence

The subjective nature of humor makes computerized humor generation a challenging task. We propose an automatic humor generation framework for filling the blanks in Mad Libs® stories, while accounting for the demographic backgrounds of the desired audience. We collect a dataset consisting of such stories, which are filled in and judged by carefully selected workers on Amazon Mechanical Turk. We build upon the BERT platform to predict location-biased word fillings in incomplete sentences, and we fine-tune BERT to classify location-specific humor in a sentence. We leverage these components to produce YodaLib, a fully-automated Mad Libs style humor generation framework, which selects and ranks appropriate candidate words and sentences in order to generate a coherent and funny story tailored to certain demographics. Our experimental results indicate that YodaLib outperforms a previous semi-automated approach proposed for this task, while also surpassing human annotators in both qualitative and quantitative analyses.
Noise Isn't Always Negative: Countering Exposure Bias in Sequence-to-Sequence Inflection Models
Authors: Garrett Nicolai, Miikka Silfverberg
Date: 2020-12-01 00:00:00
Proceeding: COLING 2020 8
Tasks: Morphological Inflection

Morphological inflection, like many sequence-to-sequence tasks, sees great performance from recurrent neural architectures when data is plentiful, but performance falls off sharply in lower-data settings. We investigate one aspect of neural seq2seq models that we hypothesize contributes to overfitting - teacher forcing. By creating different training and test conditions, exposure bias increases the likelihood that a system too closely models its training data. Experiments show that teacher-forced models struggle to recover when they enter unknown territory. However, a simple modification to the training algorithm to more closely mimic test conditions creates models that are better able to generalize to unseen environments.
Analogy Models for Neural Word Inflection
Authors: Ling Liu, Mans Hulden
Date: 2020-12-01 00:00:00
Proceeding: COLING 2020 8
Tasks: Hallucination, LEMMA

Analogy is assumed to be the cognitive mechanism speakers resort to in order to inflect an unknown form of a lexeme based on knowledge of other words in a language. In this process, an analogy is formed between word forms within an inflectional paradigm but also across paradigms. As neural network models for inflection are typically trained only on lemma-target form pairs, we propose three new ways to provide neural models with additional source forms to strengthen analogy-formation, and compare our methods to other approaches in the literature. We show that the proposed methods of providing a Transformer sequence-to-sequence model with additional analogy sources in the input are consistently effective, and improve upon recent state-of-the-art results on 46 languages, particularly in low-resource settings. We also propose a method to combine the analogy-motivated approach with data hallucination or augmentation. We find that the two approaches are complementary to each other and combining the two approaches is especially helpful when the training data is extremely limited.
Computational Modeling of Affixoid Behavior in Chinese Morphology
Authors: Yu-Hsiang Tseng, Shu-Kai Hsieh, Pei-Yi Chen, Sara Court
Date: 2020-12-01 00:00:00
Proceeding: COLING 2020 8

The morphological status of affixes in Chinese has long been a matter of debate. How one might apply the conventional criteria of free/bound and content/function features to distinguish word-forming affixes from bound roots in Chinese is still far from clear. Issues involving polysemy and diachronic dynamics further blur the boundaries. In this paper, we propose three quantitative features in a computational model of affixoid behavior in Mandarin Chinese. The results show that, except for in a very few cases, there are no clear criteria that can be used to identify an affix{'}s status in an isolating language like Chinese. A diachronic check using contextualized embeddings with the WordNet Sense Inventory also demonstrates the possible role of the polysemy of lexical roots across diachronic settings.
One Comment from One Perspective: An Effective Strategy for Enhancing Automatic Music Comment
Authors: Tengfei Huo, Zhiqiang Liu, Jinchao Zhang, Jie zhou
Date: 2020-12-01 00:00:00
Proceeding: COLING 2020 8
Tasks: Comment Generation, Diversity

The automatic generation of music comments is of great significance for increasing the popularity of music and the music platform{'}s activity. In human music comments, there exists high distinction and diverse perspectives for the same song. In other words, for a song, different comments stem from different musical perspectives. However, to date, this characteristic has not been considered well in research on automatic comment generation. The existing methods tend to generate common and meaningless comments. In this paper, we propose an effective multi-perspective strategy to enhance the diversity of the generated comments. The experiment results on two music comment datasets show that our proposed model can effectively generate a series of diverse music comments based on different perspectives, which outperforms state-of-the-art baselines by a substantial margin.
Autoregressive Affective Language Forecasting: A Self-Supervised Task
Authors: Matthew Matero, H. Andrew Schwartz
Date: 2020-12-01 00:00:00
Proceeding: COLING 2020 8

Human natural language is mentioned at a specific point in time while human emotions change over time. While much work has established a strong link between language use and emotional states, few have attempted to model emotional language in time. Here, we introduce the task of \textit{affective language forecasting} {--} predicting future change in language based on past changes of language, a task with real-world applications such as treating mental health or forecasting trends in consumer confidence. We establish some of the fundamental autoregressive characteristics of the task (necessary history size, static versus dynamic length, varying time-step resolutions) and then build on popular sequence models for \textit{words} to instead model sequences of \textit{language-based emotion in time}. Over a novel Twitter dataset of 1,900 users and weekly + daily scores for 6 emotions and 2 additional linguistic attributes, we find a novel dual-sequence GRU model with decayed hidden states achieves best results ($r = .66$) significantly out-predicting, e.g., a moving averaging based on the past time-steps ($r = .49$). We make our anonymized dataset as well as task setup and evaluation code available for others to build on.
Solving Math Word Problems with Multi-Encoders and Multi-Decoders
Authors: Yibin Shen, Cheqing Jin
Date: 2020-12-01 00:00:00
Proceeding: COLING 2020 8
Tasks: Decoder, Math

Math word problems solving remains a challenging task where potential semantic and mathematical logic need to be mined from natural language. Although previous researches employ the Seq2Seq technique to transform text descriptions into equation expressions, most of them achieve inferior performance due to insufficient consideration in the design of encoder and decoder. Specifically, these models only consider input/output objects as sequences, ignoring the important structural information contained in text descriptions and equation expressions. To overcome those defects, a model with multi-encoders and multi-decoders is proposed in this paper, which combines sequence-based encoder and graph-based encoder to enhance the representation of text descriptions, and generates different equation expressions via sequence-based decoder and tree-based decoder. Experimental results on the dataset Math23K show that our model outperforms existing state-of-the-art methods.
Semantic Role Labeling with Heterogeneous Syntactic Knowledge
Authors: Qingrong Xia, Rui Wang, Zhenghua Li, Yue Zhang, Min Zhang
Date: 2020-12-01 00:00:00
Proceeding: COLING 2020 8
Tasks: Semantic Role Labeling

Recently, due to the interplay between syntax and semantics, incorporating syntactic knowledge into neural semantic role labeling (SRL) has achieved much attention. Most of the previous syntax-aware SRL works focus on explicitly modeling homogeneous syntactic knowledge over tree outputs. In this work, we propose to encode \textit{heterogeneous} syntactic knowledge for SRL from both explicit and implicit representations. First, we introduce graph convolutional networks to explicitly encode multiple heterogeneous dependency parse trees. Second, we extract the implicit syntactic representations from syntactic parser trained with heterogeneous treebanks. Finally, we inject the two types of heterogeneous syntax-aware representations into the base SRL model as extra inputs. We conduct experiments on two widely-used benchmark datasets, i.e., Chinese Proposition Bank 1.0 and English CoNLL-2005 dataset. Experimental results show that incorporating heterogeneous syntactic knowledge brings significant improvements over strong baselines. We further conduct detailed analysis to gain insights on the usefulness of heterogeneous (vs. homogeneous) syntactic knowledge and the effectiveness of our proposed approaches for modeling such knowledge.
Leveraging WordNet Paths for Neural Hypernym Prediction
Authors: Yejin Cho, Juan Diego Rodriguez, Yifan Gao, Katrin Erk
Date: 2020-12-01 00:00:00
Proceeding: COLING 2020 8
Tasks: Decoder, Prediction

We formulate the problem of hypernym prediction as a sequence generation task, where the sequences are taxonomy paths in WordNet. Our experiments with encoder-decoder models show that training to generate taxonomy paths can improve the performance of direct hypernym prediction. As a simple but powerful model, the hypo2path model achieves state-of-the-art performance, outperforming the best benchmark by 4.11 points in hit-at-one (H@1).
When Beards Start Shaving Men: A Subject-object Resolution Test Suite for Morpho-syntactic and Semantic Model Introspection
Authors: Patricia Fischer, Dani{\"e}l de Kok, Erhard Hinrichs
Date: 2020-12-01 00:00:00
Proceeding: COLING 2020 8
Tasks: Dependency Parsing, Object, Prepositional Phrase Attachment, Sentence

In this paper, we introduce the SORTS Subject-Object Resolution Test Suite of German minimal sentence pairs for model introspection. The full test suite consists of 18,502 transitive clauses with manual annotations of 8 word order patterns, 5 morphological and syntactic and 11 semantic property classes. The test suite has been constructed such that sentences are minimal pairs with respect to a property class. Each property has been selected with a particular focus on its effect on subject-object resolution, the second-most error-prone task within syntactic parsing of German after prepositional phrase attachment (Fischer et al., 2019). The size and detail of annotations make the test suite a valuable resource for natural language processing applications with syntactic and semantic tasks. We use dependency parsing to demonstrate how the test suite allows insights into the process of subject-object resolution. Based on the test suite annotations, word order and case syncretism can be identified as most important factors that affect subject-object resolution.
Modality Enriched Neural Network for Metaphor Detection
Authors: Mingyu Wan, Baixi Xing
Date: 2020-12-01 00:00:00
Proceeding: COLING 2020 8
Tasks: Deep Learning

Metaphor as a cognitive mechanism in human{'}s conceptual system manifests itself an effective way for language communication. Although being intuitively sensible for human, metaphor detection is still a challenging task due to the subtle ontological differences between metaphorical and non-metaphorical expressions. This work proposes a modality enriched deep learning model for tackling this unsolved issue. It provides a new perspective for understanding metaphor as a modality shift, as in {`}sweet voice{'}. It also attempts to enhance metaphor detection by combining deep learning with effective linguistic insight. Extending the work at Wan et al. (2020), we concatenate word sensorimotor scores (Lynott et al., 2019) with word vectors as the input of attention-based Bi-LSTM using a benchmark dataset{--}the VUA corpus. The experimental results show great F1 improvement (above 0.5{\%}) of the proposed model over other methods in record, demonstrating the usefulness of leveraging modality norms for metaphor detection.
Coordination Boundary Identification without Labeled Data for Compound Terms Disambiguation
Authors: Yuya Sawada, Takashi Wada, Takayoshi Shibahara, Hiroki Teranishi, Shuhei Kondo, Hiroyuki Shindo, Taro Watanabe, Yuji Matsumoto
Date: 2020-12-01 00:00:00
Proceeding: COLING 2020 8
Tasks: Word Embeddings

We propose a simple method for nominal coordination boundary identification. As the main strength of our method, it can identify the coordination boundaries without training on labeled data, and can be applied even if coordination structure annotations are not available. Our system employs pre-trained word embeddings to measure the similarities of words and detects the span of coordination, assuming that conjuncts share syntactic and semantic similarities. We demonstrate that our method yields good results in identifying coordinated noun phrases in the GENIA corpus and is comparable to a recent supervised method for the case when the coordinator conjoins simple noun phrases.
Learning Semantic Correspondences from Noisy Data-text Pairs by Local-to-Global Alignments
Authors: Feng Nie, Jinpeng Wang, Chin-Yew Lin
Date: 2020-12-01 00:00:00
Proceeding: COLING 2020 8
Tasks: Data-to-Text Generation, Text Generation

Learning semantic correspondences between structured input data (e.g., slot-value pairs) and associated texts is a core problem for many downstream NLP applications, e.g., data-to-text generation. Large-scale datasets recently proposed for generation contain loosely corresponding data text pairs, where part of spans in text cannot be aligned to its incomplete paired input. To learn semantic correspondences from such datasets, we propose a two-stage local-to-global alignment (L2GA) framework. First, a local model based on multi-instance learning is applied to build alignments for texts spans that can be directly grounded to its paired structured input. Then, a novel global model built upon a memory-guided conditional random field (CRF) layer aims to infer missing alignments for text spans which not supported by paired incomplete inputs, where the memory is designed to leverage alignment clues provided by the local model to strengthen the global model. In this way, the local model and global model can work jointly to learn semantic correspondences in the same framework. Experimental results show that our proposed method can be generalized to both restaurant and computer domains and improve the alignment accuracy.
Event Coreference Resolution with their Paraphrases and Argument-aware Embeddings
Authors: Yutao Zeng, Xiaolong Jin, Saiping Guan, Jiafeng Guo, Xueqi Cheng
Date: 2020-12-01 00:00:00
Proceeding: COLING 2020 8
Tasks: coreference-resolution, Coreference Resolution, Event Coreference Resolution

Event coreference resolution aims to classify all event mentions that refer to the same real-world event into the same group, which is necessary to information aggregation and many downstream applications. To resolve event coreference, existing methods usually calculate the similarities between event mentions and between specific kinds of event arguments. However, they fail to accurately identify paraphrase relations between events and may suffer from error propagation while extracting event components (i.e., event mentions and their arguments). Therefore, we propose a new model based on Event-specific Paraphrases and Argument-aware Semantic Embeddings, thus called EPASE, for event coreference resolution. EPASE recognizes deep paraphrase relations in an event-specific context of sentences and can cover event paraphrases of more situations, bringing about a better generalization. Additionally, the embeddings of argument roles are encoded into event embedding without relying on a fixed number and type of arguments, which results in the better scalability of EPASE. Experiments on both within- and cross-document event coreference demonstrate its consistent and significant superiority compared to existing methods.
VICTR: Visual Information Captured Text Representation for Text-to-Vision Multimodal Tasks
Authors: Caren Han, Siqu Long, Siwen Luo, Kunze Wang, Josiah Poon
Date: 2020-12-01 00:00:00
Proceeding: COLING 2020 8
Tasks: Dependency Parsing, Sentence

Text-to-image multimodal tasks, generating/retrieving an image from a given text description, are extremely challenging tasks since raw text descriptions cover quite limited information in order to fully describe visually realistic images. We propose a new visual contextual text representation for text-to-image multimodal tasks, VICTR, which captures rich visual semantic information of objects from the text input. First, we use the text description as initial input and conduct dependency parsing to extract the syntactic structure and analyse the semantic aspect, including object quantities, to extract the scene graph. Then, we train the extracted objects, attributes, and relations in the scene graph and the corresponding geometric relation information using Graph Convolutional Networks, and it generates text representation which integrates textual and visual semantic information. The text representation is aggregated with word-level and sentence-level embedding to generate both visual contextual word and sentence representation. For the evaluation, we attached VICTR to the state-of-the-art models in text-to-image generation.VICTR is easily added to existing models and improves across both quantitative and qualitative aspects.
Interactive Key-Value Memory-augmented Attention for Image Paragraph Captioning
Authors: Chunpu Xu, Yu Li, Chengming Li, Xiang Ao, Min Yang, Jinwen Tian
Date: 2020-12-01 00:00:00
Proceeding: COLING 2020 8
Tasks: Decoder, Image Paragraph Captioning

Image paragraph captioning (IPC) aims to generate a fine-grained paragraph to describe the visual content of an image. Significant progress has been made by deep neural networks, in which the attention mechanism plays an essential role. However, conventional attention mechanisms tend to ignore the past alignment information, which often results in problems of repetitive captioning and incomplete captioning. In this paper, we propose an Interactive key-value Memory- augmented Attention model for image Paragraph captioning (IMAP) to keep track of the attention history (salient objects coverage information) along with the update-chain of the decoder state and therefore avoid generating repetitive or incomplete image descriptions. In addition, we employ an adaptive attention mechanism to realize adaptive alignment from image regions to caption words, where an image region can be mapped to an arbitrary number of caption words while a caption word can also attend to an arbitrary number of image regions. Extensive experiments on a benchmark dataset (i.e., Stanford) demonstrate the effectiveness of our IMAP model.
Geo-Aware Image Caption Generation
Authors: Sofia Nikiforova, Tejaswini Deoskar, Denis Paperno, Yoad Winter
Date: 2020-12-01 00:00:00
Proceeding: COLING 2020 8
Tasks: Caption Generation, Image Captioning, World Knowledge

Standard image caption generation systems produce generic descriptions of images and do not utilize any contextual information or world knowledge. In particular, they are unable to generate captions that contain references to the geographic context of an image, for example, the location where a photograph is taken or relevant geographic objects around an image location. In this paper, we develop a geo-aware image caption generation system, which incorporates geographic contextual information into a standard image captioning pipeline. We propose a way to build an image-specific representation of the geographic context and adapt the caption generation network to produce appropriate geographic names in the image descriptions. We evaluate our system on a novel captioning dataset that contains contextualized captions and geographic metadata and achieve substantial improvements in BLEU, ROUGE, METEOR and CIDEr scores. We also introduce a new metric to assess generated geographic references directly and empirically demonstrate our system{'}s ability to produce captions with relevant and factually accurate geographic referencing.
Bridge the Gap: High-level Semantic Planning for Image Captioning
Authors: Chenxi Yuan, Yang Bai, Chun Yuan
Date: 2020-12-01 00:00:00
Proceeding: COLING 2020 8
Tasks: Image Captioning, Vocal Bursts Intensity Prediction

Recent image captioning models have made much progress for exploring the multi-modal interaction, such as attention mechanisms. Though these mechanisms can boost the interaction, there are still two gaps between the visual and language domains: (1) the gap between the visual features and textual semantics, (2) the gap between the disordering of visual features and the ordering of texts. To bridge the gaps we propose a high-level semantic planning (HSP) mechanism that incorporates both a semantic reconstruction and an explicit order planning. We integrate the planning mechanism to the attention based caption model and propose the High-level Semantic PLanning based Attention Network (HS-PLAN). First, an attention based reconstruction module is designed to reconstruct the visual features with high-level semantic information. Then we apply a pointer network to serialize the features and obtain the explicit order plan to guide the generation. Experiments conducted on MS COCO show that our model outperforms previous methods and achieves the state-of-the-art performance of 133.4{\%} CIDEr-D score.