pwc-archive/papers-with-abstracts huggingface dataset:
Aligned Dual Channel Graph Convolutional Network for Visual Question Answering
Authors: Qingbao Huang, Jielong Wei, Yi Cai, Changmeng Zheng, Junying Chen, Ho-fung Leung, Qing Li
Date: 2020-07-01 00:00:00
Proceeding: ACL 2020 6
Tasks: Question Answering, Visual Question Answering, Visual Question Answering (VQA)

Visual question answering aims to answer the natural language question about a given image. Existing graph-based methods only focus on the relations between objects in an image and neglect the importance of the syntactic dependency relations between words in a question. To simultaneously capture the relations between objects in an image and the syntactic dependency relations between words in a question, we propose a novel dual channel graph convolutional network (DC-GCN) for better combining visual and textual advantages. The DC-GCN model consists of three parts: an I-GCN module to capture the relations between objects in an image, a Q-GCN module to capture the syntactic dependency relations between words in a question, and an attention alignment module to align image representations and question representations. Experimental results show that our model achieves comparable performance with the state-of-the-art approaches.
Multimodal Neural Graph Memory Networks for Visual Question Answering
Authors: Mahmoud Khademi
Date: 2020-07-01 00:00:00
Proceeding: ACL 2020 6
Tasks: Graph Neural Network, Question Answering, Visual Question Answering, Visual Question Answering (VQA)

We introduce a new neural network architecture, Multimodal Neural Graph Memory Networks (MN-GMN), for visual question answering. The MN-GMN uses graph structure with different region features as node attributes and applies a recently proposed powerful graph neural network model, Graph Network (GN), to reason about objects and their interactions in an image. The input module of the MN-GMN generates a set of visual features plus a set of encoded region-grounded captions (RGCs) for the image. The RGCs capture object attributes and their relationships. Two GNs are constructed from the input module using the visual features and encoded RGCs. Each node of the GNs iteratively computes a question-guided contextualized representation of the visual/textual information assigned to it. Then, to combine the information from both GNs, the nodes write the updated representations to an external spatial memory. The final states of the memory cells are fed into an answer module to predict an answer. Experiments show MN-GMN rivals the state-of-the-art models on Visual7W, VQA-v2.0, and CLEVR datasets.
Refer360$^\circ$: A Referring Expression Recognition Dataset in 360$^\circ$ Images
Authors: Volkan Cirik, Taylor Berg-Kirkpatrick, Louis-Philippe Morency
Date: 2020-07-01 00:00:00
Proceeding: ACL 2020 6
Tasks: Referring Expression

We propose a novel large-scale referring expression recognition dataset, Refer360{\mbox{$^\circ$}}, consisting of 17,137 instruction sequences and ground-truth actions for completing these instructions in 360{\mbox{$^\circ$}} scenes. Refer360{\mbox{$^\circ$}} differs from existing related datasets in three ways. First, we propose a more realistic scenario where instructors and the followers have partial, yet dynamic, views of the scene {--} followers continuously modify their field-of-view (FoV) while interpreting instructions that specify a final target location. Second, instructions to find the target location consist of multiple steps for followers who will start at random FoVs. As a result, intermediate instructions are strongly grounded in object references, and followers must identify intermediate FoVs to find the final target location correctly. Third, the target locations are neither restricted to predefined objects nor chosen by annotators; instead, they are distributed randomly across scenes. This {``}point anywhere{''} approach leads to more linguistically complex instructions, as shown in our analyses. Our examination of the dataset shows that Refer360{\mbox{$^\circ$}} manifests linguistically rich phenomena in a language grounding task that poses novel challenges for computational modeling of language, vision, and navigation.
Predicting the Growth of Morphological Families from Social and Linguistic Factors
Authors: Valentin Hofmann, Janet Pierrehumbert, Hinrich Sch{\"u}tze
Date: 2020-07-01 00:00:00
Proceeding: ACL 2020 6

We present the first study that examines the evolution of morphological families, i.e., sets of morphologically related words such as {``}trump{''}, {``}antitrumpism{''}, and {``}detrumpify{''}, in social media. We introduce the novel task of Morphological Family Expansion Prediction (MFEP) as predicting the increase in the size of a morphological family. We create a ten-year Reddit corpus as a benchmark for MFEP and evaluate a number of baselines on this benchmark. Our experiments demonstrate very good performance on MFEP.
Semi-supervised Contextual Historical Text Normalization
Authors: Peter Makarov, Simon Clematide
Date: 2020-07-01 00:00:00
Proceeding: ACL 2020 6
Tasks: Language Modeling, Language Modelling, Text Normalization

Historical text normalization, the task of mapping historical word forms to their modern counterparts, has recently attracted a lot of interest (Bollmann, 2019; Tang et al., 2018; Lusetti et al., 2018; Bollmann et al., 2018;Robertson and Goldwater, 2018; Bollmannet al., 2017; Korchagina, 2017). Yet, virtually all approaches suffer from the two limitations: 1) They consider a fully supervised setup, often with impractically large manually normalized datasets; 2) Normalization happens on words in isolation. By utilizing a simple generative normalization model and obtaining powerful contextualization from the target-side language model, we train accurate models with unlabeled historical data. In realistic training scenarios, our approach often leads to reduction in manually normalized data at the same accuracy levels.
DoQA - Accessing Domain-Specific FAQs via Conversational QA
Authors: Jon Ander Campos, Arantxa Otegi, Aitor Soroa, Jan Deriu, Mark Cieliebak, Eneko Agirre
Date: 2020-07-01 00:00:00
Proceeding: ACL 2020 6
Tasks: Conversational Question Answering, Information Retrieval, Question Answering, Retrieval, Transfer Learning

The goal of this work is to build conversational Question Answering (QA) interfaces for the large body of domain-specific information available in FAQ sites. We present DoQA, a dataset with 2,437 dialogues and 10,917 QA pairs. The dialogues are collected from three Stack Exchange sites using the Wizard of Oz method with crowdsourcing. Compared to previous work, DoQA comprises well-defined information needs, leading to more coherent and natural conversations with less factoid questions and is multi-domain. In addition, we introduce a more realistic information retrieval (IR) scenario where the system needs to find the answer in any of the FAQ documents. The results of an existing, strong, system show that, thanks to transfer learning from a Wikipedia QA dataset and fine tuning on a single FAQ domain, it is possible to build high quality conversational QA systems for FAQs without in-domain training data. The good results carry over into the more challenging IR scenario. In both cases, there is still ample room for improvement, as indicated by the higher human upperbound.
Multi-source Meta Transfer for Low Resource Multiple-Choice Question Answering
Authors: Ming Yan, Hao Zhang, Di Jin, Joey Tianyi Zhou
Date: 2020-07-01 00:00:00
Proceeding: ACL 2020 6
Tasks: Domain Adaptation, Logical Reasoning, Machine Reading Comprehension, Meta-Learning, Multiple-choice, Multiple Choice Question Answering (MCQA), Question Answering, Reading Comprehension, Unsupervised Domain Adaptation

Multiple-choice question answering (MCQA) is one of the most challenging tasks in machine reading comprehension since it requires more advanced reading comprehension skills such as logical reasoning, summarization, and arithmetic operations. Unfortunately, most existing MCQA datasets are small in size, which increases the difficulty of model learning and generalization. To address this challenge, we propose a multi-source meta transfer (MMT) for low-resource MCQA. In this framework, we first extend meta learning by incorporating multiple training sources to learn a generalized feature representation across domains. To bridge the distribution gap between training sources and the target, we further introduce the meta transfer that can be integrated into the multi-source meta training. More importantly, the proposed MMT is independent of backbone language models. Extensive experiments demonstrate the superiority of MMT over state-of-the-arts, and continuous improvements can be achieved on different backbone networks on both supervised and unsupervised domain adaptation settings.
Premise Selection in Natural Language Mathematical Texts
Authors: Deborah Ferreira, Andr{\'e} Freitas
Date: 2020-07-01 00:00:00
Proceeding: ACL 2020 6
Tasks: Link Prediction

The discovery of supporting evidence for addressing complex mathematical problems is a semantically challenging task, which is still unexplored in the field of natural language processing for mathematical text. The natural language premise selection task consists in using conjectures written in both natural language and mathematical formulae to recommend premises that most likely will be useful to prove a particular statement. We propose an approach to solve this task as a link prediction problem, using Deep Convolutional Graph Neural Networks. This paper also analyses how different baselines perform in this task and shows that a graph structure can provide higher F1-score, especially when considering multi-hop premise selection.
Improving Image Captioning with Better Use of Caption
Authors: Zhan Shi, Xu Zhou, Xipeng Qiu, Xiaodan Zhu
Date: 2020-07-01 00:00:00
Proceeding: ACL 2020 6
Tasks: Caption Generation, Image Captioning, Inductive Bias, Multi-Task Learning, TAG

Image captioning is a multimodal problem that has drawn extensive attention in both the natural language processing and computer vision community. In this paper, we present a novel image captioning architecture to better explore semantics available in captions and leverage that to enhance both image representation and caption generation. Our models first construct caption-guided visual relationship graphs that introduce beneficial inductive bias using weakly supervised multi-instance learning. The representation is then enhanced with neighbouring and contextual nodes with their textual and visual features. During generation, the model further incorporates visual relationships using multi-task learning for jointly predicting word and object/predicate tag sequences. We perform extensive experiments on the MSCOCO dataset, showing that the proposed framework significantly outperforms the baselines, resulting in the state-of-the-art performance under a wide range of evaluation metrics. The code of our paper has been made publicly available.
A Two-Step Approach for Implicit Event Argument Detection
Authors: Zhisong Zhang, Xiang Kong, Zhengzhong Liu, Xuezhe Ma, Eduard Hovy
Date: 2020-07-01 00:00:00
Proceeding: ACL 2020 6
Tasks: Sentence, Vocal Bursts Valence Prediction

In this work, we explore the implicit event argument detection task, which studies event arguments beyond sentence boundaries. The addition of cross-sentence argument candidates imposes great challenges for modeling. To reduce the number of candidates, we adopt a two-step approach, decomposing the problem into two sub-problems: argument head-word detection and head-to-span expansion. Evaluated on the recent RAMS dataset (Ebner et al., 2020), our model achieves overall better performance than a strong sequence labeling baseline. We further provide detailed error analysis, presenting where the model mainly makes errors and indicating directions for future improvements. It remains a challenge to detect implicit arguments, calling for more future work of document-level modeling for this task.
Machine Reading of Historical Events
Authors: Or Honovich, Lucas Torroba Hennigen, Omri Abend, Shay B. Cohen
Date: 2020-07-01 00:00:00
Proceeding: ACL 2020 6
Tasks: Information Retrieval, Reading Comprehension, Retrieval

Machine reading is an ambitious goal in NLP that subsumes a wide range of text understanding capabilities. Within this broad framework, we address the task of machine reading the time of historical events, compile datasets for the task, and develop a model for tackling it. Given a brief textual description of an event, we show that good performance can be achieved by extracting relevant sentences from Wikipedia, and applying a combination of task-specific and general-purpose feature embeddings for the classification. Furthermore, we establish a link between the historical event ordering task and the event focus time task from the information retrieval literature, showing they also provide a challenging test case for machine reading algorithms.
Do Transformers Need Deep Long-Range Memory?
Authors: Jack Rae, Ali Razavi
Date: 2020-07-01 00:00:00
Proceeding: ACL 2020 6
Tasks: Deep Attention, Language Modelling

Deep attention models have advanced the modelling of sequential data across many domains. For language modelling in particular, the Transformer-XL {---} a Transformer augmented with a long-range memory of past activations {---} has been shown to be state-of-the-art across a variety of well-studied benchmarks. The Transformer-XL incorporates a long-range memory at every layer of the network, which renders its state to be thousands of times larger than RNN predecessors. However it is unclear whether this is necessary. We perform a set of interventions to show that comparable performance can be obtained with 6X fewer long range memories and better performance can be obtained by limiting the range of attention in lower layers of the network.
Understanding Advertisements with BERT
Authors: Kanika Kalra, Bhargav Kurma, Silpa Vadakkeeveetil Sreelatha, Manasi Patwardhan, Kar, Shirish e
Date: 2020-07-01 00:00:00
Proceeding: ACL 2020 6
Tasks: Image Captioning, Sentence, Sentence-Pair Classification

We consider a task based on CVPR 2018 challenge dataset on advertisement (Ad) understanding. The task involves detecting the viewer{'}s interpretation of an Ad image captured as text. Recent results have shown that the embedded scene-text in the image holds a vital cue for this task. Motivated by this, we fine-tune the base BERT model for a sentence-pair classification task. Despite utilizing the scene-text as the only source of visual information, we could achieve a hit-or-miss accuracy of 84.95{\%} on the challenge test data. To enable BERT to process other visual information, we append image captions to the scene-text. This achieves an accuracy of 89.69{\%}, which is an improvement of 4.7{\%}. This is the best reported result for this task.
Non-Linear Instance-Based Cross-Lingual Mapping for Non-Isomorphic Embedding Spaces
Authors: Goran Glava{\v{s}}, Ivan Vuli{\'c}
Date: 2020-07-01 00:00:00
Proceeding: ACL 2020 6
Tasks: Bilingual Lexicon Induction, Cross-Lingual Word Embeddings, Translation, Word Embeddings

We present InstaMap, an instance-based method for learning projection-based cross-lingual word embeddings. Unlike prior work, it deviates from learning a single global linear projection. InstaMap is a non-parametric model that learns a non-linear projection by iteratively: (1) finding a globally optimal rotation of the source embedding space relying on the Kabsch algorithm, and then (2) moving each point along an instance-specific translation vector estimated from the translation vectors of the point{'}s nearest neighbours in the training dictionary. We report performance gains with InstaMap over four representative state-of-the-art projection-based models on bilingual lexicon induction across a set of 28 diverse language pairs. We note prominent improvements, especially for more distant language pairs (i.e., languages with non-isomorphic monolingual spaces).
Temporally-Informed Analysis of Named Entity Recognition
Authors: Shruti Rijhwani, Daniel Preotiuc-Pietro
Date: 2020-07-01 00:00:00
Proceeding: ACL 2020 6
Tasks: named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER)

Natural language processing models often have to make predictions on text data that evolves over time as a result of changes in language use or the information described in the text. However, evaluation results on existing data sets are seldom reported by taking the timestamp of the document into account. We analyze and propose methods that make better use of temporally-diverse training data, with a focus on the task of named entity recognition. To support these experiments, we introduce a novel data set of English tweets annotated with named entities. We empirically demonstrate the effect of temporal drift on performance, and how the temporal information of documents can be used to obtain better models compared to those that disregard temporal information. Our analysis gives insights into why this information is useful, in the hope of informing potential avenues of improvement for named entity recognition as well as other NLP tasks under similar experimental setups.
Learning Web-based Procedures by Reasoning over Explanations and Demonstrations in Context
Authors: Shashank Srivastava, Oleks Polozov, R, Nebojsa Jojic, Christopher Meek
Date: 2020-07-01 00:00:00
Proceeding: ACL 2020 6
Tasks: Language Acquisition, Program Synthesis, Semantic Parsing

We explore learning web-based tasks from a human teacher through natural language explanations and a single demonstration. Our approach investigates a new direction for semantic parsing that models explaining a demonstration in a context, rather than mapping explanations to demonstrations. By leveraging the idea of inverse semantics from program synthesis to reason backwards from observed demonstrations, we ensure that all considered interpretations are consistent with executable actions in any context, thus simplifying the problem of search over logical forms. We present a dataset of explanations paired with demonstrations for web-based tasks. Our methods show better task completion rates than a supervised semantic parsing baseline (40{\%} relative improvement on average), and are competitive with simple exploration-and-demonstration based methods, while requiring no exploration of the environment. In learning to align explanations with demonstrations, basic properties of natural language syntax emerge as learned behavior. This is an interesting example of pragmatic language acquisition without any linguistic annotation.
In Neural Machine Translation, What Does Transfer Learning Transfer?
Authors: Alham Fikri Aji, Nikolay Bogoychev, Kenneth Heafield, Rico Sennrich
Date: 2020-07-01 00:00:00
Proceeding: ACL 2020 6
Tasks: Learning Word Embeddings, Machine Translation, Transfer Learning, Translation, Word Embeddings

Transfer learning improves quality for low-resource machine translation, but it is unclear what exactly it transfers. We perform several ablation studies that limit information transfer, then measure the quality impact across three language pairs to gain a black-box understanding of transfer learning. Word embeddings play an important role in transfer learning, particularly if they are properly aligned. Although transfer learning can be performed without embeddings, results are sub-optimal. In contrast, transferring only the embeddings but nothing else yields catastrophic results. We then investigate diagonal alignments with auto-encoders over real languages and randomly generated sequences, finding even randomly generated sequences as parents yield noticeable but smaller gains. Finally, transfer learning can eliminate the need for a warm-up phase when training transformer models in high resource language pairs.
Translationese as a Language in ``Multilingual'' NMT
Authors: Parker Riley, Isaac Caswell, Markus Freitag, David Grangier
Date: 2020-07-01 00:00:00
Proceeding: ACL 2020 6
Tasks: Machine Translation, NMT, Sentence, TAG, Translation

Machine translation has an undesirable propensity to produce {``}translationese{''} artifacts, which can lead to higher BLEU scores while being liked less by human raters. Motivated by this, we model translationese and original (i.e. natural) text as separate languages in a multilingual model, and pose the question: can we perform zero-shot translation between original source text and original target text? There is no data with original source and original target, so we train a sentence-level classifier to distinguish translationese from original target text, and use this classifier to tag the training data for an NMT model. Using this technique we bias the model to produce more natural outputs at test time, yielding gains in human evaluation scores on both accuracy and fluency. Additionally, we demonstrate that it is possible to bias the model to produce translationese and game the BLEU score, increasing it while decreasing human-rated quality. We analyze these outputs using metrics measuring the degree of translationese, and present an analysis of the volatility of heuristic-based train-data tagging.
Automated Evaluation of Writing -- 50 Years and Counting
Authors: Beata Beigman Klebanov, Nitin Madnani
Date: 2020-07-01 00:00:00
Proceeding: ACL 2020 6
Tasks: Automated Writing Evaluation

In this theme paper, we focus on Automated Writing Evaluation (AWE), using Ellis Page{'}s seminal 1966 paper to frame the presentation. We discuss some of the current frontiers in the field and offer some thoughts on the emergent uses of this technology.
On Forgetting to Cite Older Papers: An Analysis of the ACL Anthology
Authors: Marcel Bollmann, Desmond Elliott
Date: 2020-07-01 00:00:00
Proceeding: ACL 2020 6

The field of natural language processing is experiencing a period of unprecedented growth, and with it a surge of published papers. This represents an opportunity for us to take stock of how we cite the work of other researchers, and whether this growth comes at the expense of {``}forgetting{''} about older literature. In this paper, we address this question through bibliographic analysis. By looking at the age of outgoing citations in papers published at selected ACL venues between 2010 and 2019, we find that there is indeed a tendency for recent papers to cite more recent work, but the rate at which papers older than 15 years are cited has remained relatively stable.
Returning the N to NLP: Towards Contextually Personalized Classification Models
Authors: Lucie Flek
Date: 2020-07-01 00:00:00
Proceeding: ACL 2020 6
Tasks: General Classification

Most NLP models today treat language as universal, even though socio- and psycholingustic research shows that the communicated message is influenced by the characteristics of the speaker as well as the target audience. This paper surveys the landscape of personalization in natural language processing and related fields, and offers a path forward to mitigate the decades of deviation of the NLP tools from sociolingustic findings, allowing to flexibly process the {``}natural{''} language of each user rather than enforcing a uniform NLP treatment. It outlines a possible direction to incorporate these aspects into neural NLP models by means of socially contextual personalization, and proposes to shift the focus of our evaluation strategies accordingly.