pwc-archive/papers-with-abstracts huggingface dataset:
Data Augmentation with Sentence Recombination Method for Semi-supervised Text Classification
Authors: Anonymous
Date: 2021-11-16 00:00:00
Proceeding: ACL ARR November 2021 11
Tasks: Data Augmentation, Semi-Supervised Text Classification, Sentence, text-classification, Text Classification

As the need of large amount of time and expertise to obtain enough labeled data, semi-supervised learning has received much attention to utilize both labeled and unlabeled data. In this paper, we present SeRe: a Sentence Recombination method to augment training data for semi-supervised text classification. SeRe makes full use of the similarities between sentences in different samples through the grouping and recombining process to form rich and varied training data. SeRe generates data from three combinations, including labeled, unlabeled, and mixed data. Meanwhile, SeRe combines the self-training framework to improve the quality of augmented training data iteratively. We apply SeRe to text classification tasks and conduct extensive experiments on four publicly available benchmarks. Experimental results show that SeRe achieves new state-of-the-art performances on all of them.
Weakly Supervised Medical Entity Extraction and Linking for Chief Complaints
Authors: Anonymous
Date: 2021-11-16 00:00:00
Proceeding: ACL ARR November 2021 11

A Chief complaint (CC) is the reason for the medical visit as stated in the patient's own words. It helps medical professionals to quickly understand a patient's situation, and also serves as a short summary for medical text mining. However, chief complaint records often take a variety of entering methods, resulting in a wide variation of medical notations, which makes it difficult to standardize across different medical institutions for record keeping or text mining. In this study, we propose a weakly supervised method to automatically extract and link entities in chief complaints in the absence of human annotation. We first adopt a split-and-match algorithm to produce weak annotations, including entity mention spans and class labels, on 1.2 million real-world de-identified and IRB approved chief complaint records. Then we train a BERT-based model with generated weak labels to locate entity mentions in chief complaint text and link them to a pre-defined ontology. We conducted extensive experiments and the results showed that our Weakly Supervised Entity Extraction and Linking (WeSEEL) method produced superior performance over previous methods without any human annotation.
Text Complexity And Linguistic Features: Is The Relationship Multilingual?
Authors: Anonymous
Date: 2021-11-16 00:00:00
Proceeding: ACL ARR November 2021 11
Tasks: Diversity

Text complexity assessment is a challenging task requiring various linguistic aspects to be taken into consideration.  A large number of studies have been introduced in this field. Nevertheless, as the methods and corpora are quite diverse, it may be hard to draw general conclusions as to the effectiveness of linguistic information for evaluating text complexity due to the diversity of methods and corpora. Moreover, a cross-lingual impact of different features on various datasets has not been investigated.  We experimentally assessed seven commonly used feature types on six corpora for text complexity employing four common machine learning models. We showed which feature types can significantly improve the performance and analyzed their impact according to the dataset characteristics, language, and origin.
Phrase-aware Unsupervised Constituency Parsing
Authors: Anonymous
Date: 2021-11-16 00:00:00
Proceeding: ACL ARR November 2021 11
Tasks: Constituency Parsing, Language Modeling, Language Modelling, Masked Language Modeling

Recent studies have achieved inspiring success in unsupervised grammar induction using masked language modeling (MLM) as the proxy task. Despite their high accuracy in identifying low-level structures, prior arts tend to struggle in capturing high-level structures like clauses, since the MLM task usually only requires information from local context. In this work, we revisit LM-based constituency parsing from a phrase-centered perspective. Inspired by the natural reading process of human, we propose to regularize the parser with phrases extracted by an unsupervised phrase tagger to help the LM model quickly manage low-level structures. For a better understanding of high-level structures, we propose a phrase-guided masking strategy for LM to emphasize more on reconstructing non-phrase words. We show that the initial phrase regularization serves as an effective bootstrap, and phrase-guided masking improves the identification of high-level structures. Experiments on the public benchmark with two different backbone models demonstrate the effectiveness and generality of our method.
PoliSe: Reinforcing Politeness using User Sentiment for Customer Care Response Generation
Authors: Anonymous
Date: 2021-11-16 00:00:00
Proceeding: ACL ARR November 2021 11
Tasks: Decoder, Response Generation, Sentiment Analysis

The interaction between a consumer and the customer service representative greatly contributes to the overall customer experience. Therefore, to ensure customers' comfort and retention, it is important that customer service agents and chatbots connect with users on social, cordial and empathetic planes. In the current work, we automatically identify the sentiment of the user and transform the neutral responses into polite responses conforming to the sentiment and the conversational history. Our technique is basically a reinforced multi-task network- the primary task being 'polite response generation' and the secondary task being 'sentiment analysis'- that uses a Transformer based encoder-decoder. We use sentiment annotated conversations from Twitter as the training data. The detailed evaluation shows that our proposed approach attains superior performance compared to the baseline models.
Continual Few-shot Relation Learning via Embedding Space Regularization and Data Augmentation
Authors: Anonymous
Date: 2021-11-16 00:00:00
Proceeding: ACL ARR September 2021 9
Tasks: Data Augmentation, Relation

Existing continual relation learning (CRL) methods rely on plenty of labeled training data for learning a new task, which can be hard to acquire in real scenario as getting large and representative labeled data is often expensive and time-consuming. It is therefore necessary for the model to learn novel relational patterns with very few labeled data while avoiding catastrophic forgetting of previous task knowledge. In this paper, we formulate this challenging yet practical problem as continual few-shot relation learning (CFRL). Based on the finding that learning for new emerging few-shot tasks often results in feature distributions that are incompatible with previous tasks' learned distributions, we propose a novel method based on embedding space regularization and data augmentation. Our method generalizes to new few-shot tasks and avoids catastrophic forgetting of previous tasks by enforcing extra constraints on the relational embeddings and by adding extra {relevant} data in a self-supervised manner. With extensive experiments we demonstrate that our method can significantly outperform previous state-of-the-art methods in CFRL task settings.
Towards Fully Self-Supervised Learning of Knowledge from Unstructured Text
Authors: Anonymous
Date: 2021-11-16 00:00:00
Proceeding: ACL ARR November 2021 11
Tasks: Knowledge Probing, Self-Supervised Learning

Pre-trained language models (PLMs) like BERT have made significant progress in various downstream NLP tasks. However, recent works find that PLMs are short in acquiring knowledge from the unstructured text, by asking models to do cloze-style tests. 
To understand the internal behavior of PLMs in retrieving knowledge, we firstly define knowledge-baring tokens and knowledge-free tokens for unstructured text and manually label on some samples. Then, we find that PLMs are more likely to predict incorrectly on K-B tokens and attend less attention to those tokens inside the self-attention module.  Based on these observations, we develop two solutions to help the model learn more knowledge from the unstructured text in a fully self-supervised manner.
Experiments on knowledge probing tasks show the effectiveness of the proposed methods. To our knowledge, we are the first to explore fully self-supervised learning of knowledge in continue pre-training.
FRSUM: Towards Faithful Abstractive Summarization via Enhancing Factual Robustness
Authors: Anonymous
Date: 2021-11-16 00:00:00
Proceeding: ACL ARR November 2021 11
Tasks: Abstractive Text Summarization

Though current Seq2Seq summarization models are capable of generating fluent and grammatical summaries, they are still suffering from the unfaithful generation problem.In this paper, we study the faithfulness of existing systems from a new perspective of factual robustness which is the ability to correctly generate  factual information over  adversarial unfaithful information.We first define the measurement of a model's factual robustness as its success rate to defend against adversarial attacks when generating factual information. The factual robustness analysis on a wide range of current systems shows its good consistency with human judgments on faithfulness.Inspired by these findings, we propose to improve a model's faithfulness by enhancing its factual robustness.Specifically, we propose a novel training strategy, namely FRSUM, which teaches the model to defend against both explicit adversarial samples and implicit factual adversarial perturbations.Extensive automatic and human evaluation results show that FRSUM consistently improves the faithfulness of various Seq2Seq models, such as T5, BART and PEGASUS, and reduces up to 41\% target errors in summaries.
Using Interactive Feedback to Improve the Accuracy and Explainability of Question Answering Systems Post-Deployment
Authors: Anonymous
Date: 2021-11-16 00:00:00
Proceeding: ACL ARR November 2021 11
Tasks: Question Answering, Retrieval

Most research on question answering focuses on the pre-deployment stage; i.e., building an accurate model for deployment.
In this paper, we ask the question: Can we improve QA systems further post-deployment based on user interactions? 
We focus on two kinds of improvements: 1) improving the QA system's performance itself, and 2) providing the model with the ability to explain the correctness or incorrectness of an answer.
We collect a retrieval-based QA dataset, FeebackQA, which contains interactive feedback from users. We collect this dataset by deploying a base QA system to crowdworkers who then engage with the system and provide feedback on the quality of its answers.
The feedback contains both structured ratings and unstructured natural language explanations.
We train a neural model with this feedback data that can generate explanations and re-score answer candidates. We show that usage of the feedback data improves the accuracy of the QA system, and helps users make informed decisions about the correctness of answers.\footnote{We will make both the data and the code public.}
ICLEA: Interactive Contrastive Learning for Self-supervised Entity Alignment
Authors: Anonymous
Date: 2021-11-16 00:00:00
Proceeding: ACL ARR November 2021 11
Tasks: Contrastive Learning, Entity Alignment, Knowledge Graphs

Self-supervised entity alignment (EA) aims to link equivalent entities across different knowledge graphs (KGs) without seed alignments. The current SOTA self-supervised EA method draws inspiration from contrastive learning, originally designed in computer vision based on instance discrimination and contrastive loss, and suffers from two shortcomings. Firstly, it puts unidirectional emphasis on pushing sampled negative entities far away rather than pulling positively aligned pairs close, as is done in the well-established supervised EA. Secondly, KGs contain rich side information (e.g., entity description), and how to effectively leverage those information has not been adequately investigated in self-supervised EA. In this paper, we propose an interactive contrastive learning model for self-supervised EA. The model encodes not only structures and semantics of entities (including entity name, entity description, and entity neighborhood), but also conducts cross-KG contrastive learning by building pseudo-aligned entity pairs. Experimental results show that our approach outperforms previous best self-supervised results by a large margin (over 9% average improvement) and performs on par with previous SOTA supervised counterparts, demonstrating the effectiveness of the interactive contrastive learning for self-supervised EA.
ROCK: A Causal Inference Framework for Reasoning about Commonsense Causality
Authors: Anonymous
Date: 2021-11-16 00:00:00
Proceeding: ACL ARR November 2021 11
Tasks: Causal Inference

Commonsense causality reasoning (CCR) aims at identifying plausible causes and effects in natural language descriptions that deemed reasonable by an average person. Although being of great academic and practical interest, this problem is still shadowed by the lack of a well-posed theoretical framework; existing work usually relies on various notions of correlation and is susceptible to confounding co-occurrences. This paper articulates the central question of CCR and develops a novel framework, ROCK, to Reason O(A)bout Commonsense K(C)ausality based on classical causal inference principles. ROCK leverages temporal signals as incidental supervision, and makes use of temporal propensities that are analogous to propensity scores for balancing confounding effects. We implement a modular zero-shot pipeline which is effective and demonstrates good potential for CCR on various datasets.
Fantastic Questions and Where to Find Them: FairytaleQA--An Authentic Dataset for Narrative Comprehension
Authors: Anonymous
Date: 2021-11-16 00:00:00
Proceeding: ACL ARR November 2021 11
Tasks: Benchmarking, Question Answering

Question answering (QA) is a fundamental means to facilitate assessment and training of narrative comprehension skills for both machines and young children, yet there is scarcity of high-quality QA datasets carefully designed to serve this purpose. In particular, existing datasets rarely distinguish fine-grained reading skills, such as the understanding of varying narrative elements. Drawing from education domains where QA is also used to train children's narrative comprehension, we introduce FairytaleQA, a dataset focusing on narrative comprehension of kindergarten to eighth grade students. Generated by educational experts based on an evidence-based theoretical framework, FairytaleQA  consists of 10,580 explicit and implicit questions derived from 278 children-friendly stories, covering seven types of narrative elements/relations.
Our dataset is valuable in two folds: First, with annotations on particular reading skills required for answering each question, FairytaleQA decomposes the otherwise scarce performance into multiple analysis dimensions that are consistent to human-language-learning assessment. We ran existing QA models on our dataset, and confirmed that this annotation helps assess models' fine-grained learning skills. Second, the dataset supports generating questions (QG) in the education domain. Through benchmarking with QG models, we show that the QG model trained on FairytaleQA is capable of asking high-quality and more diverse questions.
Learn More from Less: Improving Conversational Recommender Systems via Contextual and Time-Aware Modeling
Authors: Anonymous
Date: 2021-11-16 00:00:00
Proceeding: ACL ARR November 2021 11
Tasks: Recommendation Systems

Conversational Recommender Systems (CRS) aims to perform recommendations through interactive conversations. Prior work on CRS tends to incorporate more external knowledge to enhance performance. Given the fact that too much extra knowledge introduces the difficulty to balance among them and degenerates the generalizability, we propose to fully discover and extract the internal knowledge from the context. We capture both entity-level and contextual-level representations to jointly model user preferences for the recommendation, where a time-aware attention is designed to emphasize the recently appeared items in entity-level representations. We further use the pre-trained BART to initialize the generation module to alleviate the data scarcity and enhance the context modeling. Experiments on two public CRS datasets show that our model achieves comparable performance with less external knowledge and generalizes well to other domains. Further analyses demonstrate the effectiveness of our model in different scenarios.
Schema-Free Dependency Parsing via Sequence Generation
Authors: Anonymous
Date: 2021-11-16 00:00:00
Proceeding: ACL ARR November 2021 11
Tasks: Decoder, Dependency Parsing, Language Modelling

Dependency parsing aims to extract syntactic dependency structure or semantic dependency structure for sentences. Existing methods suffer the drawbacks of lacking universality or highly relying on the auxiliary decoder. To remedy these drawbacks, we propose to achieve universal and schema-free Dependency Parsing (DP) via Sequence Generation (SG) DPSG by utilizing only the pre-trained language model (PLM) without any auxiliary structures or parsing algorithms. We first explore different serialization designing strategies for converting parsing structures into sequences. Then we design dependency units and concatenate these units into the sequence for DPSG. Thanks to the high flexibility of the sequence generation, our DPSG can achieve both syntactic DP and semantic DP using a single model. By concatenating the prefix to indicate the specific schema with the sequence, our DPSG can even accomplish the multi-schemata parsing. The effectiveness of our DPSG is demonstrated by the experiments on widely used DP benchmarks, i.e., PTB, CODT, SDP15, and SemEval16. DPSG achieves comparable results with the first-tier methods on all the benchmarks and even the state-of-the-art (SOTA) performance in CODT and SemEval16. This paper demonstrates our DPSG has the potential to be a new parsing paradigm. We will release our codes upon acceptance.
Learning to Learn Recognising Biomedical Entities from Multiple Domains with Task Hardness
Authors: Anonymous
Date: 2021-11-16 00:00:00
Proceeding: ACL ARR November 2021 11
Tasks: Domain Adaptation, Few-Shot Learning, NER

Few-shot learning has been a big challenge for many classification tasks, where the final classifier is trained only with a few examples. This problem amplifies when we apply the few-shot setup to recognising named entity from different domains, i.e., few-shot domain adaption for NER. In this paper, we present a simple yet effective MAML-based NER model that can effectively leverage the task hardness information to improve the adaptability of the learnt model in the few-shot setting. Experimental results on biomedical datasets show that our model can achieve significant performance improvement over the recently published MetaNER model.
AlephBERT: Language Model Pre-training and Evaluation from Sub-Word to Sentence Level
Authors: Anonymous
Date: 2021-11-16 00:00:00
Proceeding: ACL ARR November 2021 11
Tasks: Language Modeling, Language Modelling, Sentence

Large Pre-trained Language Models (PLMs) have become ubiquitous in the development of language understanding technology and lie at the heart of many artificial intelligence advances.
While advances reported for English using PLMs are unprecedented, reported advances using PLMs for Hebrew are few and far between.
The problem is twofold.
First, so far, Hebrew resources for training large language models are not of the same magnitude as their English counterparts.
Second, there are no accepted benchmarks to evaluate the progress of Hebrew PLMs on, and in particular, sub-word (morphological) tasks.
We aim to remedy both aspects.
We present AlephBERT, a large PLM for Modern Hebrew, trained on larger vocabulary and a larger dataset than any Hebrew PLM before.
Moreover, we introduce a novel language-agnostic architecture that can recover all of the sub-word morphological segments encoded in contextualized word embedding vectors.
Based on this new morphological component we offer a new PLM evaluation suite consisting of multiple tasks and benchmarks, that cover sentence level word-level and sub-word level analyses.
On all tasks, AlephBERT obtains state-of-the-art results beyond contemporary Hebrew baselines. 
We make our AlephBERT model, the morphological extraction mode, and the Hebrew evaluation suite publicly available, providing a single point of entry for assessing Hebrew PLMs.
LAGr: Label Aligned Graphs for Better Systematic Generalization in Semantic Parsing
Authors: Anonymous
Date: 2021-11-16 00:00:00
Proceeding: ACL ARR November 2021 11
Tasks: Semantic Parsing, Systematic Generalization

Semantic parsing is the task of producing structured meaning representations for natural language sentences. 
Recent research has pointed out that the commonly-used sequence-to-sequence (seq2seq) semantic parsers struggle to generalize systematically, i.e. to handle examples that require recombining known knowledge in novel settings. In this work, we show that better systematic generalization can be achieved by producing the meaning representation directly as a graph and not as a sequence. To this end we propose LAGr (Label Aligned Graphs), a general framework to produce semantic parses by independently predicting node and edge labels for a complete multi-layer input-aligned graph. The strongly-supervised LAGr algorithm requires aligned graphs as inputs, whereas weakly-supervised LAGr infers alignments for originally unaligned target graphs using approximate maximum-a-posteriori inference. Experiments demonstrate that LAGr achieves significant improvements in systematic generalization upon the baseline seq2seq parsers in both strongly- and weakly-supervised settings.
Identifying Semantically Difficult Samples to Improve Text Classification
Authors: Anonymous
Date: 2021-11-16 00:00:00
Proceeding: ACL ARR November 2021 11
Tasks: Classification, text-classification, Text Classification

In this paper, we investigate the effect of addressing difficult samples from a given text dataset on the downstream text classification task. We define difficult samples as being non-obvious cases for text classification by analysing them in the semantic embedding space; specifically - (i) semantically similar samples that belong to different classes and (ii) semantically dissimilar samples that belong to the same class. We propose a penalty function to measure the overall difficulty score of every sample in the dataset. We conduct exhaustive experiments on 13 standard datasets to show a consistent improvement of up to 9\% and discuss qualitative results to show effectiveness of our approach in identifying difficult samples for a text classification model.
Towards Coding Social Science Datasets with Language Models
Authors: Anonymous
Date: 2021-11-16 00:00:00
Proceeding: ACL ARR November 2021 11

Researchers often rely on humans to code (label, annotate, etc.) large sets of texts. This is a highly variable task and requires a great deal of time and resources. Efforts to automate this process have achieved human-level accuracies in some cases, but often rely on thousands of hand-labeled training examples, which makes them inapplicable to small-scale research studies and still costly for large ones.  At the same time, it is well known that language models can classify text; in this work, we use OpenAI's GPT-3 as a synthetic coder, and explore what classic methodologies and metrics (such as intercoder reliability) look like in this new context.  We find that GPT-3 is able to match the performance of typical human coders and frequently outperforms humans in terms of intercoder agreement across a variety of social science tasks, suggesting that language models could be a useful tool to the social sciences.