pwc-archive/papers-with-abstracts huggingface dataset:
Multimodal and Multiresolution Speech Recognition with Transformers
Authors: Georgios Paraskevopoulos, Srinivas Parthasarathy, Aparna Khare, Shiva Sundaram
Date: 2020-07-01 00:00:00
Proceeding: ACL 2020 6
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), speech-recognition, Speech Recognition

This paper presents an audio visual automatic speech recognition (AV-ASR) system using a Transformer-based architecture. We particularly focus on the scene context provided by the visual information, to ground the ASR. We extract representations for audio features in the encoder layers of the transformer and fuse video features using an additional crossmodal multihead attention layer. Additionally, we incorporate a multitask training criterion for multiresolution ASR, where we train the model to generate both character and subword level transcriptions. Experimental results on the How2 dataset, indicate that multiresolution training can speed up convergence by around 50{\%} and relatively improves word error rate (WER) performance by upto 18{\%} over subword prediction models. Further, incorporating visual information improves performance with relative gains upto 3.76{\%} over audio only models. Our results are comparable to state-of-the-art Listen, Attend and Spell-based architectures.
Image-Chat: Engaging Grounded Conversations
Authors: Kurt Shuster, Samuel Humeau, Antoine Bordes, Jason Weston
Date: 2020-07-01 00:00:00
Proceeding: ACL 2020 6

To achieve the long-term goal of machines being able to engage humans in conversation, our models should captivate the interest of their speaking partners. Communication grounded in images, whereby a dialogue is conducted based on a given photo, is a setup naturally appealing to humans (Hu et al., 2014). In this work we study large-scale architectures and datasets for this goal. We test a set of neural architectures using state-of-the-art image and text representations, considering various ways to fuse the components. To test such models, we collect a dataset of grounded human-human conversations, where speakers are asked to play roles given a provided emotional mood or style, as the use of such traits is also a key factor in engagingness (Guo et al., 2019). Our dataset, Image-Chat, consists of 202k dialogues over 202k images using 215 possible style traits. Automatic metrics and human evaluations of engagingness show the efficacy of our approach; in particular, we obtain state-of-the-art performance on the existing IGC task, and our best performing model is almost on par with humans on the Image-Chat test set (preferred 47.7{\%} of the time).
Automatic Poetry Generation from Prosaic Text
Authors: Tim Van de Cruys
Date: 2020-07-01 00:00:00
Proceeding: ACL 2020 6
Tasks: Language Modeling, Language Modelling, Topic Models

In the last few years, a number of successful approaches have emerged that are able to adequately model various aspects of natural language. In particular, language models based on neural networks have improved the state of the art with regard to predictive language modeling, while topic models are successful at capturing clear-cut, semantic dimensions. In this paper, we will explore how these approaches can be adapted and combined to model the linguistic and literary aspects needed for poetry generation. The system is exclusively trained on standard, non-poetic text, and its output is constrained in order to confer a poetic character to the generated verse. The framework is applied to the generation of poems in both English and French, and is equally evaluated for both languages. Even though it only uses standard, non-poetic text as input, the system yields state of the art results for poetry generation.
Bridging the Structural Gap Between Encoding and Decoding for Data-To-Text Generation
Authors: Chao Zhao, Marilyn Walker, Snigdha Chaturvedi
Date: 2020-07-01 00:00:00
Proceeding: ACL 2020 6
Tasks: Data-to-Text Generation, Decoder, Text Generation

Generating sequential natural language descriptions from graph-structured data (e.g., knowledge graph) is challenging, partly because of the structural differences between the input graph and the output text. Hence, popular sequence-to-sequence models, which require serialized input, are not a natural fit for this task. Graph neural networks, on the other hand, can better encode the input graph but broaden the structural gap between the encoder and decoder, making faithful generation difficult. To narrow this gap, we propose DualEnc, a dual encoding model that can not only incorporate the graph structure, but can also cater to the linear structure of the output text. Empirical comparisons with strong single-encoder baselines demonstrate that dual encoding can significantly improve the quality of the generated text.
Simple and Effective Retrieve-Edit-Rerank Text Generation
Authors: Nabil Hossain, Marjan Ghazvininejad, Luke Zettlemoyer
Date: 2020-07-01 00:00:00
Proceeding: ACL 2020 6
Tasks: Machine Translation, Re-Ranking, Text Generation, Translation

Retrieve-and-edit seq2seq methods typically retrieve an output from the training set and learn a model to edit it to produce the final output. We propose to extend this framework with a simple and effective post-generation ranking approach. Our framework (i) retrieves several potentially relevant outputs for each input, (ii) edits each candidate independently, and (iii) re-ranks the edited candidates to select the final output. We use a standard editing model with simple task-specific re-ranking approaches, and we show empirically that this approach outperforms existing, significantly more complex methodologies. Experiments on two machine translation (MT) datasets show new state-of-art results. We also achieve near state-of-art performance on the Gigaword summarization dataset, where our analyses show that there is significant room for performance improvement with better candidate output selection in future work.
Learning to execute instructions in a Minecraft dialogue
Authors: Prashant Jayannavar, Anjali Narayan-Chen, Julia Hockenmaier
Date: 2020-07-01 00:00:00
Proceeding: ACL 2020 6
Tasks: Learning to Execute, Minecraft

The Minecraft Collaborative Building Task is a two-player game in which an Architect (A) instructs a Builder (B) to construct a target structure in a simulated Blocks World Environment. We define the subtask of predicting correct action sequences (block placements and removals) in a given game context, and show that capturing B{'}s past actions as well as B{'}s perspective leads to a significant improvement in performance on this challenging language understanding problem.
Knowledge Graph Embedding Compression
Authors: Mrinmaya Sachan
Date: 2020-07-01 00:00:00
Proceeding: ACL 2020 6
Tasks: Graph Embedding, Knowledge Graph Embedding, Representation Learning

Knowledge graph (KG) representation learning techniques that learn continuous embeddings of entities and relations in the KG have become popular in many AI applications. With a large KG, the embeddings consume a large amount of storage and memory. This is problematic and prohibits the deployment of these techniques in many real world settings. Thus, we propose an approach that compresses the KG embedding layer by representing each entity in the KG as a vector of discrete codes and then composes the embeddings from these codes. The approach can be trained end-to-end with simple modifications to any existing KG embedding technique. We evaluate the approach on various standard KG embedding evaluations and show that it achieves 50-1000x compression of embeddings with a minor loss in performance. The compressed embeddings also retain the ability to perform various reasoning tasks such as KG inference.
Low Resource Sequence Tagging using Sentence Reconstruction
Authors: Tal Perl, Sriram Chaudhury, Raja Giryes
Date: 2020-07-01 00:00:00
Proceeding: ACL 2020 6
Tasks: NER, POS, Sentence, Transfer Learning

This work revisits the task of training sequence tagging models with limited resources using transfer learning. We investigate several proposed approaches introduced in recent works and suggest a new loss that relies on sentence reconstruction from normalized embeddings. Specifically, our method demonstrates how by adding a decoding layer for sentence reconstruction, we can improve the performance of various baselines. We show improved results on the CoNLL02 NER and UD 1.2 POS datasets and demonstrate the power of the method for transfer learning with low-resources achieving 0.6 F1 score in Dutch using only one sample from it.
Weight Poisoning Attacks on Pretrained Models
Authors: Keita Kurita, Paul Michel, Graham Neubig
Date: 2020-07-01 00:00:00
Proceeding: ACL 2020 6
Tasks: Sentiment Analysis, Sentiment Classification, Spam detection

Recently, NLP has seen a surge in the usage of large pre-trained models. Users download weights of models pre-trained on large datasets, then fine-tune the weights on a task of their choice. This raises the question of whether downloading untrusted pre-trained weights can pose a security threat. In this paper, we show that it is possible to construct {``}weight poisoning{''} attacks where pre-trained weights are injected with vulnerabilities that expose {``}backdoors{''} after fine-tuning, enabling the attacker to manipulate the model prediction simply by injecting an arbitrary keyword. We show that by applying a regularization method which we call RIPPLe and an initialization procedure we call Embedding Surgery, such attacks are possible even with limited knowledge of the dataset and fine-tuning procedure. Our experiments on sentiment classification, toxicity detection, and spam detection show that this attack is widely applicable and poses a serious threat. Finally, we outline practical defenses against such attacks.
Breaking Through the 80\% Glass Ceiling: Raising the State of the Art in Word Sense Disambiguation by Incorporating Knowledge Graph Information
Authors: Michele Bevilacqua, Roberto Navigli
Date: 2020-07-01 00:00:00
Proceeding: ACL 2020 6
Tasks: All, Word Sense Disambiguation

Neural architectures are the current state of the art in Word Sense Disambiguation (WSD). However, they make limited use of the vast amount of relational information encoded in Lexical Knowledge Bases (LKB). We present Enhanced WSD Integrating Synset Embeddings and Relations (EWISER), a neural supervised architecture that is able to tap into this wealth of knowledge by embedding information from the LKB graph within the neural architecture, and to exploit pretrained synset embeddings, enabling the network to predict synsets that are not in the training set. As a result, we set a new state of the art on almost all the evaluation settings considered, also breaking through, for the first time, the 80{\%} ceiling on the concatenation of all the standard all-words English WSD evaluation benchmarks. On multilingual all-words WSD, we report state-of-the-art results by training on nothing but English.
Glyph2Vec: Learning Chinese Out-of-Vocabulary Word Embedding from Glyphs
Authors: Hong-You Chen, Sz-Han Yu, Shou-De Lin
Date: 2020-07-01 00:00:00
Proceeding: ACL 2020 6

Chinese NLP applications that rely on large text often contain huge amounts of vocabulary which are sparse in corpus. We show that characters{'} written form, \textit{Glyphs}, in ideographic languages could carry rich semantics. We present a multi-modal model, \textit{Glyph2Vec}, to tackle Chinese out-of-vocabulary word embedding problem. Glyph2Vec extracts visual features from word glyphs to expand current word embedding space for out-of-vocabulary word embedding, without the need of accessing any corpus, which is useful for improving Chinese NLP systems, especially for low-resource scenarios. Experiments across different applications show the significant effectiveness of our model.
Predicting Degrees of Technicality in Automatic Terminology Extraction
Authors: Anna H{\"a}tty, Dominik Schlechtweg, Michael Dorna, Sabine Schulte im Walde
Date: 2020-07-01 00:00:00
Proceeding: ACL 2020 6
Tasks: Term Extraction, Word Embeddings

While automatic term extraction is a well-researched area, computational approaches to distinguish between degrees of technicality are still understudied. We semi-automatically create a German gold standard of technicality across four domains, and illustrate the impact of a web-crawled general-language corpus on technicality prediction. When defining a classification approach that combines general-language and domain-specific word embeddings, we go beyond previous work and align vector spaces to gain comparative embeddings. We suggest two novel models to exploit general- vs. domain-specific comparisons: a simple neural network model with pre-computed comparative-embedding information as input, and a multi-channel model computing the comparison internally. Both models outperform previous approaches, with the multi-channel model performing best.
Verbal Multiword Expressions for Identification of Metaphor
Authors: Omid Rohanian, Marek Rei, Shiva Taslimipoor, Le An Ha
Date: 2020-07-01 00:00:00
Proceeding: ACL 2020 6

Metaphor is a linguistic device in which a concept is expressed by mentioning another. Identifying metaphorical expressions, therefore, requires a non-compositional understanding of semantics. Multiword Expressions (MWEs), on the other hand, are linguistic phenomena with varying degrees of semantic opacity and their identification poses a challenge to computational models. This work is the first attempt at analysing the interplay of metaphor and MWEs processing through the design of a neural architecture whereby classification of metaphors is enhanced by informing the model of the presence of MWEs. To the best of our knowledge, this is the first {``}MWE-aware{''} metaphor identification system paving the way for further experiments on the complex interactions of these phenomena. The results and analyses show that this proposed architecture reach state-of-the-art on two different established metaphor datasets.
Attentive Pooling with Learnable Norms for Text Representation
Authors: Chuhan Wu, Fangzhao Wu, Tao Qi, Xiaohui Cui, Yongfeng Huang
Date: 2020-07-01 00:00:00
Proceeding: ACL 2020 6

Pooling is an important technique for learning text representations in many neural NLP models. In conventional pooling methods such as average, max and attentive pooling, text representations are weighted summations of the L1 or L{\mbox{$\infty$}} norm of input features. However, their pooling norms are always fixed and may not be optimal for learning accurate text representations in different tasks. In addition, in many popular pooling methods such as max and attentive pooling some features may be over-emphasized, while other useful ones are not fully exploited. In this paper, we propose an Attentive Pooling with Learnable Norms (APLN) approach for text representation. Different from existing pooling methods that use a fixed pooling norm, we propose to learn the norm in an end-to-end manner to automatically find the optimal ones for text representation in different tasks. In addition, we propose two methods to ensure the numerical stability of the model training. The first one is scale limiting, which re-scales the input to ensure non-negativity and alleviate the risk of exponential explosion. The second one is re-formulation, which decomposes the exponent operation to avoid computing the real-valued powers of the input and further accelerate the pooling operation. Experimental results on four benchmark datasets show that our approach can effectively improve the performance of attentive pooling.
Estimating the influence of auxiliary tasks for multi-task learning of sequence tagging tasks
Authors: Fynn Schr{\"o}der, Chris Biemann
Date: 2020-07-01 00:00:00
Proceeding: ACL 2020 6
Tasks: Multi-Task Learning, Transfer Learning

Multi-task learning (MTL) and transfer learning (TL) are techniques to overcome the issue of data scarcity when training state-of-the-art neural networks. However, finding beneficial auxiliary datasets for MTL or TL is a time- and resource-consuming trial-and-error approach. We propose new methods to automatically assess the similarity of sequence tagging datasets to identify beneficial auxiliary data for MTL or TL setups. Our methods can compute the similarity between any two sequence tagging datasets, they do not need to be annotated with the same tagset or multiple labels in parallel. Additionally, our methods take tokens and their labels into account, which is more robust than only using either of them as an information source, as conducted in prior work. We empirically show that our similarity measures correlate with the change in test score of neural networks that use the auxiliary dataset for MTL to increase the main task performance. We provide an efficient, open-source implementation.
Zero-shot Text Classification via Reinforced Self-training
Authors: Zhiquan Ye, Yuxia Geng, Jiaoyan Chen, Jingmin Chen, Xiaoxiao Xu, SuHang Zheng, Feng Wang, Jun Zhang, Huajun Chen
Date: 2020-07-01 00:00:00
Proceeding: ACL 2020 6
Tasks: Classification, General Classification, reinforcement-learning, Reinforcement Learning (RL), text-classification, Text Classification, Zero-Shot Learning, Zero-Shot Text Classification

Zero-shot learning has been a tough problem since no labeled data is available for unseen classes during training, especially for classes with low similarity. In this situation, transferring from seen classes to unseen classes is extremely hard. To tackle this problem, in this paper we propose a self-training based method to efficiently leverage unlabeled data. Traditional self-training methods use fixed heuristics to select instances from unlabeled data, whose performance varies among different datasets. We propose a reinforcement learning framework to learn data selection strategy automatically and provide more reliable selection. Experimental results on both benchmarks and a real-world e-commerce dataset show that our approach significantly outperforms previous methods in zero-shot text classification
Camouflaged Chinese Spam Content Detection with Semi-supervised Generative Active Learning
Authors: Zhuoren Jiang, Zhe Gao, Yu Duan, Yangyang Kang, Changlong Sun, Qiong Zhang, Xiaozhong Liu
Date: 2020-07-01 00:00:00
Proceeding: ACL 2020 6
Tasks: Active Learning, Chinese Spam Detection, Data Augmentation, Diversity, Spam detection

We propose a Semi-supervIsed GeNerative Active Learning (SIGNAL) model to address the imbalance, efficiency, and text camouflage problems of Chinese text spam detection task. A {``}self-diversity{''} criterion is proposed for measuring the {``}worthiness{''} of a candidate for annotation. A semi-supervised variational autoencoder with masked attention learning approach and a character variation graph-enhanced augmentation procedure are proposed for data augmentation. The preliminary experiment demonstrates the proposed SIGNAL model is not only sensitive to spam sample selection, but also can improve the performance of a series of conventional active learning models for Chinese spam detection task. To the best of our knowledge, this is the first work to integrate active learning and semi-supervised generative learning for text spam detection.
Hiring Now: A Skill-Aware Multi-Attention Model for Job Posting Generation
Authors: Liting Liu, Jie Liu, Wenzheng Zhang, Ziming Chi, Wenxuan Shi, YaLou Huang
Date: 2020-07-01 00:00:00
Proceeding: ACL 2020 6
Tasks: Conditional Text Generation, Decoder, Text Generation

Writing a good job posting is a critical step in the recruiting process, but the task is often more difficult than many people think. It is challenging to specify the level of education, experience, relevant skills per the company information and job description. To this end, we propose a novel task of Job Posting Generation (JPG) which is cast as a conditional text generation problem to generate job requirements according to the job descriptions. To deal with this task, we devise a data-driven global Skill-Aware Multi-Attention generation model, named SAMA. Specifically, to model the complex mapping relationships between input and output, we design a hierarchical decoder that we first label the job description with multiple skills, then we generate a complete text guided by the skill labels. At the same time, to exploit the prior knowledge about the skills, we further construct a skill knowledge graph to capture the global prior knowledge of skills and refine the generated results. The proposed approach is evaluated on real-world job posting data. Experimental results clearly demonstrate the effectiveness of the proposed method.
HyperCore: Hyperbolic and Co-graph Representation for Automatic ICD Coding
Authors: Pengfei Cao, Yubo Chen, Kang Liu, Jun Zhao, Shengping Liu, Weifeng Chong
Date: 2020-07-01 00:00:00
Proceeding: ACL 2020 6

The International Classification of Diseases (ICD) provides a standardized way for classifying diseases, which endows each disease with a unique code. ICD coding aims to assign proper ICD codes to a medical record. Since manual coding is very laborious and prone to errors, many methods have been proposed for the automatic ICD coding task. However, most of existing methods independently predict each code, ignoring two important characteristics: Code Hierarchy and Code Co-occurrence. In this paper, we propose a Hyperbolic and Co-graph Representation method (HyperCore) to address the above problem. Specifically, we propose a hyperbolic representation method to leverage the code hierarchy. Moreover, we propose a graph convolutional network to utilize the code co-occurrence. Experimental results on two widely used datasets demonstrate that our proposed model outperforms previous state-of-the-art methods.
Hyperbolic Capsule Networks for Multi-Label Classification
Authors: Boli Chen, Xin Huang, Lin Xiao, Liping Jing
Date: 2020-07-01 00:00:00
Proceeding: ACL 2020 6
Tasks: Classification, General Classification, Multi-Label Classification, MUlTI-LABEL-ClASSIFICATION

Although deep neural networks are effective at extracting high-level features, classification methods usually encode an input into a vector representation via simple feature aggregation operations (e.g. pooling). Such operations limit the performance. For instance, a multi-label document may contain several concepts. In this case, one vector can not sufficiently capture its salient and discriminative content. Thus, we propose Hyperbolic Capsule Networks (HyperCaps) for Multi-Label Classification (MLC), which have two merits. First, hyperbolic capsules are designed to capture fine-grained document information for each label, which has the ability to characterize complicated structures among labels and documents. Second, Hyperbolic Dynamic Routing (HDR) is introduced to aggregate hyperbolic capsules in a label-aware manner, so that the label-level discriminative information can be preserved along the depth of neural networks. To efficiently handle large-scale MLC datasets, we additionally present a new routing method to adaptively adjust the capsule number during routing. Extensive experiments are conducted on four benchmark datasets. Compared with the state-of-the-art methods, HyperCaps significantly improves the performance of MLC especially on tail labels.
MOOCCube: A Large-scale Data Repository for NLP Applications in MOOCs
Authors: Jifan Yu, Gan Luo, Tong Xiao, Qingyang Zhong, Yuquan Wang, Wenzheng Feng, Junyi Luo, Chenyu Wang, Lei Hou, Juanzi Li, Zhiyuan Liu, Jie Tang
Date: 2020-07-01 00:00:00
Proceeding: ACL 2020 6

The prosperity of Massive Open Online Courses (MOOCs) provides fodder for many NLP and AI research for education applications, e.g., course concept extraction, prerequisite relation discovery, etc. However, the publicly available datasets of MOOC are limited in size with few types of data, which hinders advanced models and novel attempts in related topics. Therefore, we present MOOCCube, a large-scale data repository of over 700 MOOC courses, 100k concepts, 8 million student behaviors with an external resource. Moreover, we conduct a prerequisite discovery task as an example application to show the potential of MOOCCube in facilitating relevant research. The data repository is now available at http://moocdata.cn/data/MOOCCube.