pwc-archive/papers-with-abstracts huggingface dataset:
TableIE: Capture the Interactions among Joint Information Extraction Explicitly via Double Tables
Authors: Anonymous
Date: 2021-11-16 00:00:00
Proceeding: ACL ARR November 2021 11
Tasks: Event Extraction, named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER), Relation, Relation Extraction

Information Extraction (IE) mainly consists of three sub-tasks, Named Entity Recognition, Relation Extraction, and Event Extraction. Although the sub-tasks are highly correlated with each other, most previous works simply focus on part of them and ignore the interactions among different sub-tasks. In this paper, we propose TableIE, a double-tables based method to capture the interactions among joint information extraction (Joint IE, i.e., conduct all sub-tasks jointly) explicitly. Different from the complicated graph-based Joint IE methods, we maintain two tables for entity-relation extraction and event extraction respectively instead and implement the interactions among three IE sub-tasks explicitly. The experiment show that our TableIE model outperforms the previous state-of-the-art up to 2.5 in the ACE05 dataset.
Direct parsing to sentiment graphs
Authors: Anonymous
Date: 2021-11-16 00:00:00
Proceeding: ACL ARR November 2021 11
Tasks: Sentiment Analysis

This paper demonstrates how a graph-based semantic parser can be applied to the task of structured sentiment analysis, directly predicting sentiment graphs from text. We advance the state of the art on 4 out of 5 standard benchmark sets. We release the source code, models and predictions with the camera-ready version.
Retrieval-based Layer-wise Adaptive Transformer for Source Code Summarization
Authors: Anonymous
Date: 2021-11-16 00:00:00
Proceeding: ACL ARR November 2021 11
Tasks: Code Summarization, Retrieval, Source Code Summarization

We propose a model that learns both the sequential and the structural features of code for source code summarization. We adopt the Abstract Syntax Tree (AST) and graph convolution to model the structural information and the Transformer to model the sequential information. We convert code snippets into ASTs and apply graph convolution to obtain structurally-encoded node representations. Then, the sequences of the graph-convolutioned AST nodes are processed by the Transformer layers. Since structurally-neighboring nodes will have similar representations in graph-convolutioned trees, the Transformer layers can effectively capture not only the sequential information but also the structural information such as sentences or blocks of source code. We show that our model outperforms the state-of-the-art for source code summarization by experiments and human evaluations.
Entity-Conditioned Question Generation for Robust Attention Distribution in Neural Information Retrieval
Authors: Anonymous
Date: 2021-11-16 00:00:00
Proceeding: ACL ARR November 2021 11
Tasks: Information Retrieval, Question Generation, Question-Generation, Retrieval, Synthetic Data Generation

We show that supervised neural information retrieval (IR) models are prone to learning sparse attention patterns over passage tokens, which can result in key phrases including named entities receiving low attention weights, eventually leading to model under-performance. Using a novel targeted synthetic data generation method that identifies poorly attended entities and conditions the generation episodes on those, we teach neural IR to attend more uniformly and robustly to all entities in a given passage. On three public IR benchmarks, we empirically show that the proposed method helps improve both the model's attention patterns and retrieval performance, including in zero-shot settings.
Simulating Bandit Learning from User Feedback for Extractive Question Answering
Authors: Anonymous
Date: 2021-11-16 00:00:00
Proceeding: ACL ARR November 2021 11
Tasks: Extractive Question-Answering, Question Answering

We study learning from user feedback for extractive question answering by simulating feedback using supervised data. We cast the problem as contextual bandit learning, and analyze the characteristics of several learning scenarios with focus on reducing data annotation. We show that systems initially trained on few examples can dramatically improve given feedback from users on model-predicted answers, and that one can use existing datasets to deploy systems in new domains without any annotation effort, but instead improving the system on-the-fly via user feedback.
Self-supervised Semantic-driven Phoneme Discovery for Zero-resource Speech Recognition
Authors: Anonymous
Date: 2021-11-16 00:00:00
Proceeding: ACL ARR November 2021 11
Tasks: Phoneme Recognition, Representation Learning, Self-Supervised Learning, speech-recognition, Speech Recognition

Phonemes are defined by their relationship to words: changing a phoneme changes the word. Learning a phoneme inventory with little supervision has been a longstanding challenge with important applications to under-resourced speech technology. In this paper, we bridge the gap between the linguistic and statistical definition of phonemes and propose a novel neural discrete representation learning model for self-supervised learning of phoneme inventory with raw speech and word labels. Under mild assumptions, we prove that the phoneme inventory learned by our approach converges to the true one with an exponentially low error rate. Moreover, in experiments on TIMIT and Mboshi benchmarks, our approach consistently learns a better phoneme-level representation and achieves a lower error rate in a zero-resource phoneme recognition task than previous state-of-the-art self-supervised representation learning algorithms.
A Feasibility Study of Answer-Unaware Question Generation for Education
Authors: Anonymous
Date: 2021-11-16 00:00:00
Proceeding: ACL ARR November 2021 11
Tasks: Question Generation, Question-Generation

We conduct a feasibility study into the applicability of \textit{answer-unaware} question generation models to textbook passages. We show that a significant portion of errors in such systems arise from asking irrelevant or un-interpretable questions and that such errors can be ameliorated by providing summarized input. We find that giving these models human-written summaries instead of the original text results in a significant increase in acceptability of generated questions (33\% -> 83\%) as determined by expert annotators. We also find that, in the absence of human-written summaries, automatic summarization can serve as a good middle ground.
DISAPERE: A Dataset for Discourse Structure in Peer Review Discussions
Authors: Anonymous
Date: 2021-11-16 00:00:00
Proceeding: ACL ARR November 2021 11
Tasks: Sentence

At the foundation of scientific evaluation is the labor-intensive process of peer review. This critical task requires participants to consume vast amounts of highly technical text. Prior work has annotated different aspects of review argumentation, but discourse relations between reviews and rebuttals have yet to be examined.

We present DISAPERE, a labeled dataset of 20k sentences contained in 506 review-rebuttal pairs in English, annotated by experts. DISAPERE synthesizes label sets from prior work and extends them to include fine-grained annotation of the rebuttal sentences, characterizing the authors' stance towards review arguments, and their context in the review. Further, we annotate \textit{every} review and rebuttal sentence.

We show that discourse cues from rebuttals can shed light on the quality and interpretation of reviews. Further, an understanding of the argumentative strategies employed by the reviewers and authors provides useful signal for area chairs and other decision makers.
XLM-E: Cross-lingual Language Model Pre-training via ELECTRA
Authors: Anonymous
Date: 2021-11-16 00:00:00
Proceeding: ACL ARR November 2021 11
Tasks: Language Modeling, Language Modelling, Translation

In this paper, we introduce ELECTRA-style tasks to cross-lingual language model pre-training. Specifically, we present two pre-training tasks, namely multilingual replaced token detection, and translation replaced token detection. Besides, we pretrain the model, named as XLM-E, on both multilingual and parallel corpora. Our model outperforms the baseline models on various cross-lingual understanding tasks with much less computation cost. Moreover, analysis shows that XLM-E tends to obtain better cross-lingual transferability.
Divide and Denoise: Learning from Noisy Labels in Fine-grained Entity Typing with Cluster-wise Loss Correction
Authors: Anonymous
Date: 2021-11-16 00:00:00
Proceeding: ACL ARR November 2021 11
Tasks: Entity Typing

Fine-grained Entity Typing(FET) has witnessed great progress since distant supervision was introduced, but still suffers from label noise. Existing noise control methods applied to FET rely on predicted distribution and deals instances isolately, thus suffers from confirmation bias. In this work, We propose to tackle the two limitations with a cluster based loss correction framework named Feature Cluster Loss Correction(FCLC). FCLC first train a coarse backbone model as feature extractor and noise estimator. Then perform loss correction on each cluster to learn directly from noisy labels. Experimental results on three public datasets show FCLC achieves the best performance over existing competitive systems. Auxiliary experiments further show FCLC is stable to hyper-paramerters and even works with extream scneriaos like no clean data is available.
NSP-BERT: A Prompt-based Zero-Shot Learner Through an Original Pre-training Task —— Next Sentence Prediction
Authors: Anonymous
Date: 2021-11-16 00:00:00
Proceeding: ACL ARR November 2021 11
Tasks: Entity Linking, Language Modeling, Language Modelling, Prompt Learning, Sentence, Word Sense Disambiguation

Using prompts to utilize language models to perform various downstream tasks, also known as prompt-based learning or prompt-learning, has lately gained significant success in comparison to the pre-train and fine-tune paradigm. Nonetheless, virtually all prompt-based methods are token-level, meaning they all utilize GPT's left-to-right language model or BERT's masked language model to perform cloze-style tasks. In this paper, we attempt to accomplish several NLP tasks in the zero-shot scenario using a BERT original pre-training task abandoned by RoBERTa and other models—Next Sentence Prediction (NSP). Unlike token-level techniques, our sentence-level prompt-based method NSP-BERT does not need to fix the length of the prompt or the position to be predicted, allowing it to handle tasks such as entity linking with ease. Based on the characteristics of NSP-BERT, we offer several quick building templates for various downstream tasks. We suggest a two-stage prompt method for word sense disambiguation tasks in particular. Our samples-contrast method for mapping the labels significantly enhance the model's performance on sentence-pair tasks. On the Chinese benchmark FewCLUE, our NSP-BERT outperforms other zero-shot methods on most of these tasks and comes close to the few-shot methods. And on GLUE and other English datasets NSP-BERT is still competitive. Our code will be available on github.
An Information Theoretic Measurement of Topical Relevance in Learner Essays
Authors: Anonymous
Date: 2021-11-16 00:00:00
Proceeding: ACL ARR November 2021 11

We present a new approach to assess topical relevance in learner essays, leveraging recent advances in pretrained language models. Our approach is to generate features by calculating the normalized pointwise mutual information (NPMI) between prompts and sentences in an essay using a finetuned version of GPT-2. We demonstrate various desirable properties of this approach, including its strong performance under a variety of evaluation settings, its generalizability to novel prompts, and its interpretability.
Improving Controllable Text Generation with Position-Aware Weighted Decoding
Authors: Anonymous
Date: 2021-11-16 00:00:00
Proceeding: ACL ARR November 2021 11
Tasks: Attribute, Language Modeling, Language Modelling, Position, Text Generation

Weighted decoding methods composed of the pretrained language model (LM) and the controller have achieved promising results for controllable text generation. However, these models often suffer from a control strength/fluency trade-off problem as higher control strength is more likely to generate incoherent and repetitive text. In this paper, we illustrate this trade-off is arisen by the controller imposing the target attribute on the LM at improper positions. And we propose a novel framework based on existing weighted decoding methods called CAT-PAW, which introduces a lightweight regulator to adjust bias signals from the controller at different decoding positions. Experiments on positive sentiment control, topic control, and language detoxification show the effectiveness of our CAT-PAW upon 4 SOTA models.
All Birds with One Stone: Multi-task Learning for Inference with One Forward Pass
Authors: Anonymous
Date: 2021-11-16 00:00:00
Proceeding: ACL ARR November 2021 11
Tasks: All, Computational Efficiency, Multi-Task Learning, Scheduling, Transfer Learning

Task-specific fine-tuning of pre-trained language models like Transformers has shown their effectiveness in various NLP tasks. To achieve better storage efficiency and model performance, Multi-task Learning (MTL) has been studied to share model parameters and utilize knowledge transfer between tasks. However, in real applications where enormous numbers of tasks (e.g., large sets of labels to be classified) need to be conducted on a large corpus, the inference efficiency is still hindered by the number of tasks.
For a document with N sets of labels to be predicted, recent MTL methods with adaptive modules or prompts need to encode the input data N times to extract the hidden representation needed for the tasks. Notice that the hidden representation is not sharable between tasks, as task-specific features are extracted at very bottom layers in the Transformer. 
In this paper, we seek to maintain the computational efficiency of only requiring one forward pass for a document to get a generalized feature for all N tasks, without sacrificing overall model performance.
We design a prompt-sharing module to let the model take all tasks into considerations and output N heads simultaneously. 
We also design a dynamic task scheduling module to sample tasks according to their training progress. 
In our evaluation, we show that our method is able to outperform previous MTL state-of-the-arts and single task fine-tuning by 0.4-1.5% on GLUE benchmark dataset. We also perform comprehensive module analysis to demonstrate the effectiveness and robustness of our method.
CONJR: Conjunctive Sentence Splitter without Parsing
Authors: Anonymous
Date: 2021-11-16 00:00:00
Proceeding: ACL ARR November 2021 11
Tasks: Boundary Detection, Sentence

In this paper, we observe and address the challenges of splitting conjunctive sentences around each group of conjuncts. Most existing methods rely on parsers to identify the conjuncts in a sentence and detect the coordination boundaries. However, state-of-the-art syntactic parsers are slow and suffer from errors, especially for long and complicated sentences. In order to better solve the problems, we formulate coordination boundary detection as a sequence tagging task and propose a specialized model CONJR without using syntactic parsers. We introduce both semantic and syntactic features and a specially designed attention mechanism to capture the symmetry among the potential conjuncts. The experimental results on datasets from various domains demonstrate the effectiveness of our proposed methods.
Pair-Based Joint Learning with Relational Graph Convolutional Networks for Emotion-Cause Pair Extraction
Authors: Anonymous
Date: 2021-11-16 00:00:00
Proceeding: ACL ARR November 2021 11
Tasks: Emotion-Cause Pair Extraction

Emotion-cause pair extraction (ECPE) aims to extract the emotion clauses and the corresponding cause clauses, which have recently received more attention. Previous methods sequentially encode features with a specified order, which first encode the emotion and cause features for clause extraction and then combine them for pair extraction, leading to an imbalance in inter-task feature interaction where features extracted later have no direct contact with the former. To this end, we propose a novel joint encoding network, which generates pairs and clauses features simultaneously in a joint feature learning manner to model the causal relationship from clauses. Specifically, from a multi-relational perspective, we construct a heterogeneous undirected graph and apply the Relational Graph Convolutional Network (RGCN) to capture the complex relationship between clauses and the relationship between pairs and clauses. Experimental results show that our model achieves state-of-the-art performance on the Chinese benchmark corpus.
Same Neurons, Different Languages: Probing Morphosyntax in Multilingual Pre-trained Models
Authors: Anonymous
Date: 2021-11-16 00:00:00
Proceeding: ACL ARR November 2021 11

The success of multilingual pre-trained models in transferring knowledge cross-lingually is underpinned by their ability to learn representations shared by multiple languages even in absence of any explicit supervision. However, it remains unclear how. In this work, we conjecture that multilingual pre-trained models can derive language-universal abstractions about grammar. In particular, we investigate whether morphosyntactic information is encoded in the same subset of neurons in different languages. We conduct the first large-scale empirical study over 43 typologically diverse languages and 14 morphosyntactic categories with a state-of-the-art neuron-level probe. Our findings show that the cross-lingual overlap between neurons is significant, but its extent may vary across categories and depends on language proximity and pre-training data size.
Uncertainty-Based Joint Training For Semi-Supervised Math Word Problem
Authors: Anonymous
Date: 2021-11-16 00:00:00
Proceeding: ACL ARR November 2021 11
Tasks: Math

Math word problems (MWPs) convert natural math corpus into structured equation forms. Data sparsity is one of the main obstacles for math word understanding problem due to the high cost of human annotation efforts. However, existing work mainly start from the supervised learning perspective, making the low-resource scenario under explored. In this paper, we are the first to incorporate semi-supervised learning (SSL) framework into MWPs. We propose an uncertainty-aware unlabeled data selection strategies, which can access to reliable samples and increase the model capacity gradually. Besides, to improve the quality of pseudo equations, we incorporate two indirect supervision signals considering the semantic consistency property and grammar format constraints of generated equations. Experimental results on two benchmark MWPs datasets across different ratio of unlabeled data verify the effectiveness and generalization ability of our proposed method.
Fill-in-the-Blank: A Challenging Video Understanding Evaluation Framework
Authors: Anonymous
Date: 2021-11-16 00:00:00
Proceeding: ACL ARR November 2021 11
Tasks: Multiple-choice, Question Answering, Video Captioning, Video Question Answering, Video Understanding

We propose fill-in-the-blanks as a video understanding evaluation framework. The task tests a model's understanding of a video by requiring the model to predict a masked noun phrase in the caption of the video, given the video and the surrounding text. To this end, we introduce a novel dataset consisting of 28,000 videos and fill-in-the-blank tests with multiple correct answers. The task and the dataset are challenging for the current state-of-the-art systems to solve. This task also does not share the weaknesses of the current state of the art language-informed video understanding tasks, namely: (1) video question answering using multiple-choice questions, where models perform relatively well because they exploit linguistic biases in the task formulation; and (2) video captioning, which relies on an open-ended evaluation framework that is often inaccurate because system answers may be perceived as incorrect if they differ in form from the ground truth.
Probing Difficulty and Discrimination of Natural Language Questions With Item Response Theory
Authors: Anonymous
Date: 2021-11-16 00:00:00
Proceeding: ACL ARR November 2021 11
Tasks: Active Learning, Question Answering

Item Response Theory (IRT) has been extensively used to characterize question difficulty for human subjects in domains including cognitive psychology and education (Primi et al.,2014; Downing, 2003). In this work, we explore IRT to characterize the difficulty and discrimination of natural language questions in Question-Answering datasets. We use HotPotQA for illustration. Our analysis reveals significant variations along these traits, as well as interdependence between them. Additionally, we explore predictive models for directly estimating these traits from the text of the questions and answers. Our experiments show that it is possible to predict both difficulty and discrimination parameters for new questions, and these traits are correlated with features of questions, answers, and associated contexts. Our findings can have significant implications for the creation of new datasets and tests on the one hand and strategies such as active learning and curriculum learning on the other.
Towards Community-Driven NLP: Measuring Geographic Performance Disparities of Offensive Language Classifiers
Authors: Anonymous
Date: 2021-11-16 00:00:00
Proceeding: ACL ARR November 2021 11

Text classifiers are applied at scale in the form of one-size-fits-all solutions. Nevertheless, many studies show that many classifiers are biased regarding different languages and dialects. Both language style and content change depending on the location that it is posted. For example, states that border Mexico may be more likely to discuss issues regarding immigration from Latin America. However, several questions remain, such as ``Do changes in the style and content of text across geographic regions impact model performance?''. We introduce a novel dataset called GeoOLID with more than 13 thousand examples across 15 geographically and demographically diverse cites to address this question. Furthermore, we perform a comprehensive analysis of geographical content and stylistic differences and their interaction in causing performance disparities of Offensive Language Detection models. Overall, we find that current models do not generalize across. Likewise, we show that understanding broad dialects (e.g., African American English) is not the only predictive factor of model performance when applied to cities with large minority populations. Hence, community-specific evaluation is vital for real-world applications. Warning: This paper contains offensive language.
How do we answer complex questions: Discourse structure of long form answers
Authors: Anonymous
Date: 2021-11-16 00:00:00
Proceeding: ACL ARR November 2021 11
Tasks: Extractive Summarization, Form, Natural Questions, Sentence

Long form answers, consisting of multiple sentences, can provide nuanced and comprehensive answers to a broader set of questions. However, little prior work exists on this task. To better understand this complex task, we study the functional structure of long form answers on two datasets, Natural Questions~\cite{kwiatkowski2019natural} and ELI5~\cite{Fan2019ELI5LF}. Our main goal is to understand how humans organize information to craft complex answers. We develop an ontology of sentence-level functional roles for long form answers, and annotate 3.3k sentences in 542 examples. Our annotated data enables training a reliable role classifier that can be used for automatic analysis and thus reveals machine generated answers are structured worse than human written answers. Our data further yields an extractive summarization dataset for long form answers, giving models the ability to identify a concise answer to a complex query.