pwc-archive/papers-with-abstracts huggingface dataset:
Unsupervised Domain Adaptation for Event Detection via Meta Self-Paced Learning
Authors: Anonymous
Date: 2021-11-16 00:00:00
Proceeding: ACL ARR November 2021 11
Tasks: Domain Adaptation, Event Detection, Meta-Learning, Unsupervised Domain Adaptation

As important events in textual data are usually highly specific in terms of tasks and domains, a change in data distribution would have a significant impact on detection performance. Recent methods addressing unsupervised domain adaptation for event detection task typically extracted domain-invariant representations through combining and balancing various objectives to align the feature space between source and target domains. While effective, these methods are impractical as large-scale language models are drastically growing bigger to achieve optimal performance. To this end, we propose Meta Self-Paced Domain Adaption framework (MSP-DA) that effectively and efficiently alleviates the need for domain-specific hyperparameter tuning. By imitating the train-test dataset split based on the difficulties of source domain's samples, the model is trained through a meta-learning process that learns to weigh the importance of each labeled instance and to balance every alignment objective, simultaneously. Extensive experiments demonstrate our framework substantially improves performance on target domains, surpassing state-of-the-art approaches. Furthermore, we present detailed analyses to validate our method and provide insight into how each domain affects the learned hyperparameters.
Solving Probability and Statistics Problems by Program Synthesis
Authors: Anonymous
Date: 2021-11-16 00:00:00
Proceeding: ACL ARR November 2021 11
Tasks: Form, Program Synthesis, Prompt Engineering

We solve university level probability and statistics questions by program synthesis using OpenAI's Codex, a Transformer trained on text and fine-tuned on code. We transform course problems from MIT's 18.05 Introduction to Probability and Statistics and Harvard's STAT110 Probability into programming tasks. We then execute the generated code to get a solution. Since these course questions are grounded in probability, we often aim to have Codex generate probabilistic programs that simulate a large number of probabilistic dependencies, to compute its solution. Our approach requires prompt engineering to transform the question from its original form to an explicit, tractable form that results in a correct program and solution. To estimate the amount of work needed to translate an original question into its tractable form, we measure the similarity between original and transformed questions. Our work is the first to introduce a new dataset of university-level probability and statistics problems and solve these problems in a scalable fashion using the program synthesis capabilities of large language models.
"Is Whole Word Masking Always Better for Chinese BERT?": Probing on Chinese Grammatical Error Correction
Authors: Anonymous
Date: 2021-11-16 00:00:00
Proceeding: ACL ARR November 2021 11
Tasks: Grammatical Error Correction, Language Modeling, Language Modelling, Masked Language Modeling, Sentence

Whole word masking (WWM), which masks all subwords corresponding to a word at once, makes a better English BERT model. For the Chinese language, however, there is no subword because each token is an atomic character. The meaning of a word in Chinese is different in that a word is a compositional unit consisting of multiple characters. Such difference motivates us to investigate whether WWM leads to better context understanding ability for Chinese BERT. To achieve this, we introduce two probing tasks related to grammatical error correction and ask pretrained models to revise or insert tokens in a masked language modeling manner. We construct a dataset including labels for 19,075 tokens in 10,448 sentences. We train three Chinese BERT models with standard character-level masking (CLM), WWM, and a combination of CLM and WWM, respectively. Our major findings are as follows: First, when one character needs to be inserted or replaced, the model trained with CLM performs the best. Second, when more than one character needs to be handled, WWM is the key to better performance. Finally, when being fine-tuned on sentence-level downstream tasks, models trained with different masking strategies perform comparably.
ComSearch: Equation Searching with Combinatorial Mathematics for Solving Math Word Problems with Weak Supervision
Authors: Anonymous
Date: 2021-11-16 00:00:00
Proceeding: ACL ARR November 2021 11
Tasks: Math

Previous studies have introduced a weakly-supervised paradigm for solving math word problems requiring only the answer value annotation. While these methods search for correct value equation candidates as pseudo labels, they search among a narrow sub-space of the enormous equation space. To address this problem, we propose a novel search algorithm with combinatorial mathematics \textbf{ComSearch}, which can compress the search space by excluding mathematical equivalent equations. The compression allows the searching algorithm to enumerate all possible equations and obtain high-quality data. Experimental results show that our method achieves state-of-the-art results, especially for problems with more variables.
KESA: A Knowledge Enhanced Approach For Sentiment Analysis
Authors: Anonymous
Date: 2021-11-16 00:00:00
Proceeding: ACL ARR November 2021 11
Tasks: Sentence, Sentiment Analysis

Though some recent works focus on injecting sentiment knowledge into pre-trained language models, they usually design mask and reconstruction tasks in the post-training phase. In this paper, we aim to benefit from sentiment knowledge in a lighter way. To achieve this goal, we study sentence-level sentiment analysis and, correspondingly, propose two sentiment-aware auxiliary tasks named sentiment word cloze and conditional sentiment prediction. The first task learns to select the correct sentiment words within the input, given the overall sentiment polarity as prior knowledge. On the contrary, the second task predicts the overall sentiment polarity given the sentiment polarity of the word as prior knowledge. In addition, two kinds of label combination methods are investigated to unify multiple types of labels in each task. We argue that more information can promote the models to learn more profound semantic representation. We implement it in a straightforward way to verify this hypothesis. The experimental results demonstrate that our approach consistently outperforms pre-trained models and is additive to existing knowledge-enhanced post-trained models.
It is AI’s Turn to Ask Human a Question: Question and Answer Pair Generation for Children Storybooks in FairytaleQA Dataset
Authors: Anonymous
Date: 2021-11-16 00:00:00
Proceeding: ACL ARR November 2021 11
Tasks: Answer Generation, Question-Answer-Generation, Question Answering

Existing question answering (QA) techniques are created mainly to answer questions asked by humans. But in educational applications, teachers and parents sometimes may not know what questions they should ask best help children to develop their narrative understanding abilities. We design an automated question-answer generation (QAG) system for education purposes: given a storybook at the kindergarten to eighth-grade level, our system can automatically produce QA pairs that are capable of testing a variety of student comprehension skills. Using a new QA dataset FairytaleQA that has 278 child-friendly storybooks with 10,580 QA pairs labeled by experts, we design a novel QAG system architecture to generate QA pairs. Automatic and human evaluations show that our model outperforms state-of-the-art QAG systems. On top of our QAG system, we also build an interactive story-telling application for future real-world deployment.
Continual Prompt Tuning for Dialog State Tracking
Authors: Anonymous
Date: 2021-11-16 00:00:00
Proceeding: ACL ARR November 2021 11
Tasks: Continual Learning, dialog state tracking, Transfer Learning

A desirable dialog system should be able to continually learn new skills without forgetting old ones, and thereby adapt to new domains or tasks in its life cycle. However, continually training a model often leads to a well-known catastrophic forgetting issue. In this paper, we present Continual Prompt Tuning, a parameter-efficient framework that not only avoids forgetting but also enables knowledge transfer between tasks. To avoid forgetting, we only learn and store a few prompt tokens' embeddings for each task while freezing the backbone pre-trained model. To achieve bi-directional knowledge transfer among tasks, we propose several techniques (continual prompt initialization, query fusion, and memory replay) to transfer knowledge from preceding tasks and a memory-guided technique to transfer knowledge from subsequent tasks. Extensive experiments demonstrate the effectiveness and efficiency of our proposed method on continual learning for dialog state tracking, compared with state-of-the-art baselines.
UniSAr: A Unified Structure-Aware Autoregressive Language Model for Text-to-SQL
Authors: Anonymous
Date: 2021-11-16 00:00:00
Proceeding: ACL ARR November 2021 11
Tasks: Language Modeling, Language Modelling, Text to SQL, Text-To-SQL

Existing text-to-SQL semantic parsers are typically designed for particular settings such as handling queries that span multiple tables, domains or turns which makes them ineffective when applied to different settings. We present UniSAr (Unified Structure-Aware Autoregressive Language Model), which benefits from directly using an off-the-shelf language model architecture and demonstrates consistently high performance under different settings. Specifically, UniSAr extends existing autoregressive language models to incorporate three non-invasive extensions to make them structure-aware: (1) adding structure mark to encode database schema, conversation context, and their relationships;  (2) constrained decoding to decode well structured SQL for a given database schema; and (3) SQL completion to complete potential missing JOIN relationships in SQL based on database schema. On seven well-known text-to-SQL datasets including multi-domain, multi-table and multi-turn, UniSAr demonstrates highly comparable or better performance to the most advanced specifically-designed text-to-SQL models. Importantly, our UniSAr is non-invasive, such that other core model advances in text-to-SQL can also adopt our extensions to further enhance performance.
QubitE: Qubit Embedding for Knowledge Graph Completion
Authors: Anonymous
Date: 2021-11-16 00:00:00
Proceeding: ACL ARR November 2021 11
Tasks: Complex Query Answering, Knowledge Graph Completion, Knowledge Graph Embeddings, Knowledge Graphs, Link Prediction

Knowledge graph embeddings (KGEs) learn low-dimensional representations of entities and relations to predict missing facts based on existing ones.

Quantum-based KGEs utilise variational quantum circuits for link prediction and score triples via the probability distribution of measuring the qubit states. However, there exists another best measurement for training variational quantum circuits. Besides, current quantum-based methods ignore theoretical analysis which are essential for understanding the model performance and applying for downstream tasks such as reasoning, path query answering, complex query answering, etc.

To address measurement issue and bridge theory gap, we propose QubitE whose score of a triple is defined as the similarity between qubit states.
Here, our measurements are viewed as kernel methods to separate the qubit states, while preserving quantum adavantages.
Furthermore, we show that (1) QubitE is full-expressive; (2) QubitE can infer various relation patterns including symmetry/antisymmetry, inversion, and commutative/non-commutative composition; (3) QubitE subsumes serveral existing approaches, \eg~DistMult, pRotatE, RotatE, TransE and ComplEx; (4) QubitE owns linear space complexity and linear time complexity.

Experiments results on multiple benchmark knowledge graphs demonstrate that QubitE can achieve comparable results to the state-of-the-art classical models.
ECSpell$^{UD}$: Zero-shot Domain Adaptive Chinese Spelling Check with User Dictionary
Authors: Anonymous
Date: 2021-11-16 00:00:00
Proceeding: ACL ARR November 2021 11
Tasks: Domain Adaptation, token-classification, Token Classification

Spellers often work within a particular domain in real life. Due to lots of uncommon domain terms, experiments on our built domain specific dataset show that general models perform terribly. Inspired by the common practice of input methods, we propose to add an alterable user dictionary to handle the zero-shot domain adaption problem. Specifically, we attach a User Dictionary guided inference module (UD) to a general token classification based speller. Without extra fine-tuning, UD reveals an ability to improve the performance of all the tested spellers, especially for strong baselines. Therefore, to further make the domain adaptive speller practical, we develop a competitive general speller ECSpell which adopts the Error Consistent masking strategy to create data for pertaining. Domain experiments demonstrate that ECSpell$^{UD}$, namely ECSpell combined with UD, surpasses all the other baselines largely, even approaching the performance on the general benchmark.
All in One: A Multi-Task Learning for Emoji, Sentiment and Emotion Analysis in Code-Mixed Text
Authors: Anonymous
Date: 2021-11-16 00:00:00
Proceeding: ACL ARR November 2021 11
Tasks: All, Emotion Recognition, Multi-Task Learning

Code mixed language and emojis are being extensively used in social media to express opinions. In this paper, we propose a novel task that focuses on suggesting appropriate emojis in English-Hindi code-mixed sentences. We aim to exploit the dependency between emotion, sentiment, and emojis for building an end-to-end framework that can simultaneously identify the emotion, sentiment and emojis in code-mixed sentences. We introduce the Code-Mixed Emoji, Emotion and Sentiment aware Dataset (CMEESD) which is an extension of the SemEval 2020 Task 9. We establish strong baselines to predict the correct emojis by simultaneously identifying the emotion and sentiment of a given tweet. The sentiment and emotion prediction in turn helps for the appropriate emoji classification. Empirical results on the CMEESD dataset demonstrate that the proposed multi-task framework yields better performance over the single-task framework.
MRCLens: an MRC Dataset Bias Detection Toolkit
Authors: Anonymous
Date: 2021-11-16 00:00:00
Proceeding: ACL ARR November 2021 11
Tasks: Bias Detection, Machine Reading Comprehension, Reading Comprehension

Many recent neural models have shown remarkable empirical results in Machine Reading Comprehension, but evidence suggests sometimes the models take advantage of dataset biases to predict and fail to generalize on out-of-sample data. While many other approaches have been proposed to address this issue from the computation perspective such as new architectures or training procedures, we believe a method that allows researchers to discover biases, adjust the data or the models in an earlier stage will be beneficial. Thus, we introduce MRCLens, a toolkit which detects whether biases exist before users train the full model. For the convenience of introducing the toolkit, we also provide a categorization of common biases in MRC.
Rethinking News Text Classification from a Timeliness Perspective under the Pre-training and Fine-tuning Paradigm
Authors: Anonymous
Date: 2021-11-16 00:00:00
Proceeding: ACL ARR November 2021 11
Tasks: Classification, News Classification, text-classification, Text Classification

Pre-trained language models (PLMs) have made significant progress in NLP. News text classification is one of the most fundamental tasks in NLP, and various existing works have shown that fine-tuned on PLMs could score up to the accuracy of 98% on the target task. It seems that this task has been well-addressed. However, we discover that news timeliness can cause a massive impact on the news text classification, which drops nearly 20% points from the initial results. In this paper, we define timeliness issues in news classification and design the experiment to measure the influence. Moreover, we investigate several methods to recognize and replace obsolete vocabularies. However, the results show that it is difficult to eliminate the impact of news timeliness from the words' perspective. In addition, we propose a set of large-scale, time-sensitive news datasets to facilitate the study of this problem.
$AmbiPun$ : Generating Humorous Puns with Ambiguous Context
Authors: Anonymous
Date: 2021-11-16 00:00:00
Proceeding: ACL ARR November 2021 11
Tasks: Reverse Dictionary, Sentence

Computational humor has garnered interest of the natural language processing community due to its wide applications to real world scenarios. One way to express humor is via the use of puns. A homographic pun plays on words that are spelled the same way but have different meanings. In this paper, we propose a simple yet effective way to generate pun sentences that does not require any pun sentences to train on. Our approach is inspired by humor theories that ambiguity comes from the context rather than the pun word itself. Given a pair of definitions of a pun word, our model first produces a list of related concepts through a reverse dictionary. We then utilize one-shot GPT3 to generate context words, and then generate punning sentences that incorporate context words from both worlds.
We also investigate how the position of a pun word appearing in the sentence will influence the generated results. We compare our proposed $\textsc{AmbiPun}$ with well crafted baselines. Human evaluation shows that our method successfully generates pun 52% of the time, outperforming the the state-of-the-art model by a large margin.
What Makes The Story Forward? Inferring Commonsense Explanations as Prompts for Future Event Generation
Authors: Anonymous
Date: 2021-11-16 00:00:00
Proceeding: ACL ARR November 2021 11
Tasks: Text Generation

Future Event Generation (FEG) aims to generate fluent and reasonable future event descriptions given preceding events. It requires not only fluent text generation but also commonsense reasoning to maintain the coherence of the entire event story. However, existing FEG methods are easily trapped into repeated or general events without imposing any logical constraint to the generation process. In this paper, we propose a novel explainable FEG framework that consists of a commonsense inference model (\textsc{Im}) and an event generation model (\textsc{Gm}). The \textsc{Im}, which is pre-trained on a commonsense knowledge graph ATOMIC, learns to interpret the preceding events and conducts commonsense reasoning to reveal the character’s psychology such as intent, reaction and needs as latent variables. The \textsc{Gm} further takes the commonsense knowledge as prompts to guide and enforce the generation of logistically coherent future events. As a unique merit, the commonsense prompts can be further decoded into textual descriptions, yielding explanations for the future event. Automatic and human evaluation demonstrate that our approach can generate more coherent, specific, and logical future events than the strong baselines. All the programs and resources will be made public upon acceptance.
MSAMSum: Towards Benchmarking Multi-lingual Dialogue Summarization
Authors: Anonymous
Date: 2021-11-16 00:00:00
Proceeding: ACL ARR November 2021 11
Tasks: Benchmarking, dialogue summary, Translation

Dialogue summarization helps users capture salient information from various types of dialogues has received much attention recently. However, current works mainly focus on English dialogue summarization, leaving other languages under exploration. Therefore, we present a multi-lingual dialogue summarization dataset, namely MSAMSum, which covers dialogue-summary pairs in six languages. Specifically, we derive MSAMSum from the standard SAMSum using sophisticated translation techniques and further employ two methods to ensure the integral translation quality and summary factual consistency. Given the proposed MSAMum, we systematically set up five multi-lingual settings for this task, including a novel mix-lingual dialogue summarization setting. To illustrate the utility of our dataset, we benchmark various experiments with pre-trained models under different settings and report results in both supervised and zero-shot manners. We also discuss some future works towards this task to motivate future researches.
Are All the Datasets in Benchmark Necessary? A Pilot Study of Dataset Evaluation for Text Classification
Authors: Anonymous
Date: 2021-11-16 00:00:00
Proceeding: ACL ARR November 2021 11
Tasks: All, Sentence, text-classification, Text Classification

In this paper, we ask the research question if all the datasets in the benchmark are necessary. We approach this by first characterizing the distinguishability of datasets when comparing different systems. Experiments on 9 datasets and 36 systems show that several existing benchmark datasets contribute little to discriminating top-scoring systems, while those less used datasets exhibit impressive discriminative power. We further, taking the text classification task as a case study, investigate the possibility of predicting dataset discrimination based on its properties (e.g., average sentence length). Our preliminary experiments promisingly show that given a sufficient number of training experimental records, a meaningful predictor can be learned to estimate dataset discrimination over unseen datasets. We released all related code at Github \url{https://github.com/annonnlp-demo/acl-V2} and a new benchmark dataset for text classification based on our observations.
GradMask: Gradient-Guided Token Masking for Textual Adversarial Example Detection
Authors: Anonymous
Date: 2021-11-16 00:00:00
Proceeding: ACL ARR September 2021 9

We present a simple model-agnostic textual adversarial example detection scheme called GradMask. It uses gradient signals to detect adversarially perturbed tokens in an input sequence and occludes such tokens by a masking process. GradMask provides several advantages over existing methods including improved detection performance and a weak interpretation of its decision. Extensive evaluations on widely adopted natural language processing benchmark datasets demonstrate the efficiency and effectiveness of GradMask
When did you become so smart, oh wise one?! Sarcasm Explanation in Multi-modal Multi-party Dialogues
Authors: Anonymous
Date: 2021-11-16 00:00:00
Proceeding: ACL ARR November 2021 11

Indirect speech such as sarcasm achieves a constellation of discourse goals in human communication. While the indirectness of figurative language warrants speakers to achieve certain pragmatic goals, it is challenging for AI agents to comprehend such idiosyncrasies of human communication. Though sarcasm identification has been a well-explored topic in dialogue analysis, for conversational systems to truly grasp a conversation's innate meaning and generate appropriate responses, simply detecting sarcasm is not enough; it is vital to explain its underlying sarcastic connotation to capture its true essence. In this work, we study the discourse structure of sarcastic conversations and propose a novel task -- Sarcasm Explanation in Dialogue (SED). Set in a multimodal and code-mixed setting, the task aims to generate natural language explanations of satirical conversations. To this end, we curate WITS, a new dataset to support our task. We propose MAF (Modality Aware Fusion), a multimodal context-aware attention and global information fusion module to capture multimodality and use it to benchmark WITS. The proposed attention module surpasses the traditional multimodal fusion baselines and reports the best performance on almost all metrics. Lastly, we carry out detailed analysis both quantitatively and qualitatively.