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Scientific documents are replete with measurements mentioned in various formats and styles. As such, in a document with multiple quantities and measured entities, the task of associating each quantity to its corresponding measured entity is challenging. Thus, it is necessary to have a method to efficiently extract all measurements and attributes related to them. To this end, in this paper, we propose a novel model for the task of measurement relation extraction (MRE) whose goal is to recognize the relation between measured entities, quantities and conditions mentioned in a document. Our model employs a deep translation-based architecture to dynamically induce the important words in the document to classify the relation between a pair of entities. Furthermore, we introduce a novel regularization technique based on Information Bottleneck (IB) to filter out the noisy information from the induced set of important words. Our experiments on the recent SemEval 2021 Task 8 datasets reveal the effectiveness of the proposed model.

IJCNLP-AACL 2021

DPR at SemEval-2021 Task 8: Dynamic Path Reasoning for Measurement Relation Extraction

measurement relation extraction

document structure

regularization

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The great event is jointly organized by the Association for Computational Linguistics (ACL) and Asian Federation of Natural Language Processing (AFNLP).

workshop paper

Previous math word problem solvers following the encoder-decoder paradigm fail to explicitly incorporate essential math symbolic constraints, leading to unexplainable and unreasonable predictions. Herein, we propose Neural-Symbolic Solver (NS-Solver) to explicitly and seamlessly incorporate different levels of symbolic constraints by auxiliary tasks. Our NS-Solver consists of a problem reader to encode problems, a programmer to generate symbolic equations, and a symbolic executor to obtain answers. Along with target expression supervision, our solver is also optimized via 4 new auxiliary objectives to enforce different symbolic reasoning: a) self-supervised number prediction task predicting both number quantity and number locations; b) commonsense constant prediction task predicting what prior knowledge (e.g. how many legs
a chicken has) is required; c) program consistency checker computing the semantic loss between predicted equation and target equation to ensure reasonable equation mapping; d) duality exploiting task exploiting the quasi-duality between symbolic equation generation and problem’s part-of-speech generation to enhance the understanding ability of a solver. Besides, to provide a more realistic and challenging benchmark for developing a universal and scalable solver, we also construct a new largescale MWP benchmark CM17K consisting of 4 kinds of MWPs (arithmetic, one-unknown linear, one-unknown non-linear, equation set) with more than 17K samples. Extensive experiments on Math23K and our CM17k demonstrate the superiority of our NS-Solver compared to state-of-the-art methods.

Neural-Symbolic Solver for Math Word Problems with Auxiliary Tasks

Recently, the performance of Pre-trained Language Models (PLMs) has been significantly improved by injecting knowledge facts to enhance their abilities of language understanding. For medical domains, the background knowledge sources are especially useful, due to the massive medical terms and their complicated relations are difficult to understand in text.
In this work, we introduce SMedBERT, a medical PLM trained on large-scale medical corpora, incorporating deep structured semantic knowledge from neighbours of linked-entity.
In SMedBERT, the mention-neighbour hybrid attention is proposed to learn heterogeneous-entity information, which infuses the semantic representations of entity types into the homogeneous neighbouring entity structure.
Apart from knowledge integration as external features, we propose to employ the neighbors of linked-entities in the knowledge graph as additional global contexts of text mentions, allowing them to communicate via shared neighbors, thus enrich their semantic representations.
Experiments demonstrate that SMedBERT significantly outperforms strong baselines in various knowledge-intensive Chinese medical tasks. It also improves the performance of other tasks such as question answering, question matching and natural language inference.

SMedBERT: A Knowledge-Enhanced Pre-trained Language Model with Structured Semantics for Medical Text Mining

When evaluating an article and the claims it makes, a critical reader must be able to assess where the 
information presented comes from, and whether the various claims are mutually consistent and support the conclusion. This motivates the study of \emph{claim provenance}, which seeks to trace and explain the origins of claims. In this paper, we introduce new techniques to model and reason about the provenance of \emph{multiple} interacting claims, including how to capture \emph{fine-grained} information about the context. Our solution hinges on first identifying the sentences that potentially contain important external information. We then develop a query generator with our novel \emph{rank-aware cross attention} mechanism, which aims at generating metadata for the source article, based on the context and the signals collected from a search engine. This establishes relevant search queries, and it allows us to obtain source article candidates for each identified sentence and propose an ILP based algorithm to infer the best sources. We experiment with a newly created evaluation dataset, Politi-Prov, based on fact-checking articles from \url{www.politifact.com}; our experimental results show that our solution leads to a significant improvement over baselines.

What is Your Article Based On? Inferring Fine-grained Provenance

There is content such as hate speech, offensive, toxic or aggressive documents, which are perceived differently by their consumers. They are commonly identified using classifiers solely based on textual content that generalize pre-agreed meanings of difficult problems. Such models provide the same results for each user, which leads to high misclassification rate observable especially for contentious, aggressive documents. Both document controversy and user nonconformity require new solutions. Therefore, we propose novel personalized approaches that respect individual beliefs expressed by either user conformity-based measures or various embeddings of their previous text annotations. We found that only a few annotations of most controversial documents are enough for all our personalization methods to significantly outperform classic, generalized solutions. The more controversial the content, the greater the gain. The personalized solutions may be used to efficiently filter unwanted aggressive content in the way adjusted to a given person.

Controversy and Conformity: from Generalized to Personalized Aggressiveness Detection

As more and more product reviews are posted in both text and images, Multimodal Review Analysis (MRA) becomes an attractive research topic. Among the existing review analysis tasks, helpfulness prediction on review text has become predominant due to its importance for e-commerce platforms and online shops, i.e. helping customers quickly acquire useful product information. This paper proposes a new task Multimodal Review Helpfulness Prediction (MRHP) aiming to analyze the review helpfulness from text and visual modalities. Meanwhile, a novel Multi-perspective Coherent Reasoning method (MCR) is proposed to solve the MRHP task, which conducts joint reasoning over texts and images from both the product and the review, and aggregates the signals to predict the review helpfulness. Concretely, we first propose a product-review coherent reasoning module to measure the intra- and inter-modal coherence between the target product and the review. In addition, we also devise an intra-review coherent reasoning module to identify the coherence between the text content and images of the review, which is a piece of strong evidence for review helpfulness prediction. To evaluate the effectiveness of MCR, we present two newly collected multimodal review datasets as benchmark evaluation resources for the MRHP task. Experimental results show that our MCR method can lead to a performance increase of up to 8.5% as compared to the best performing text-only model. The source code and datasets can be obtained from https://github.com/jhliu17/MCR.

Multi-perspective Coherent Reasoning for Helpfulness Prediction of Multimodal Reviews

In this paper, we propose Shallow Aggressive Decoding (SAD) to improve the online inference efficiency of the Transformer for instantaneous Grammatical Error Correction (GEC). SAD optimizes the online inference efficiency for GEC by two innovations: 1) it aggressively decodes as many tokens as possible in parallel instead of always decoding only one token in each step to improve computational parallelism; 2) it uses a shallow decoder instead of the conventional Transformer architecture with balanced encoder-decoder depth to reduce the computational cost during inference. Experiments in both English and Chinese GEC benchmarks show that aggressive decoding could yield identical predictions to greedy decoding but with significant speedup for online inference. Its combination with the shallow decoder could offer an even higher online inference speedup over the powerful Transformer baseline without quality loss. Not only does our approach allow a single model to achieve the state-of-the-art results in English GEC benchmarks: 66.4 F0.5 in the CoNLL-14 and 72.9 F0.5 in the BEA-19 test set with an almost 10x online inference speedup over the Transformer-big model, but also it is easily adapted to other languages. Our code is available at https://github.com/AutoTemp/Shallow-Aggressive-Decoding.

Instantaneous Grammatical Error Correction with Shallow Aggressive Decoding

This paper explores the task of Difficulty-Controllable Question Generation (DCQG), which aims at generating questions with required difficulty levels. Previous research on this task mainly defines the difficulty of a question as whether it can be correctly answered by a Question Answering (QA) system, lacking interpretability and controllability. In our work, we redefine question difficulty as the number of inference steps required to answer it and argue that Question Generation (QG) systems should have stronger control over the logic of generated questions. To this end, we propose a novel framework that progressively increases question difficulty through step-by-step rewriting under the guidance of an extracted reasoning chain. A dataset is automatically constructed to facilitate the research, on which extensive experiments are conducted to test the performance of our method.

Guiding the Growth: Difficulty-Controllable Question Generation through Step-by-Step Rewriting

The multimodality problem has become a major challenge of existing non-autoregressive generation (NAG) systems. A common solution often resorts to sequence-level knowledge distillation by rebuilding the training dataset through autoregressive generation (hereinafter known as ``teacher AG"). The success of such methods may largely depend on a latent assumption, i.e., the teacher AG is superior to the NAG model. However, in this work, we experimentally reveal that this assumption does not always hold for the text generation tasks like text summarization and story ending generation. To provide a feasible solution to the multimodality problem of NAG, we propose incorporating linguistic structure (Part-of-Speech sequence in particular) into NAG inference instead of relying on teacher AG. More specifically, the proposed POS-constrained Parallel Decoding (POSPD) method aims at providing a specific POS sequence to constrain the NAG model during decoding. Our experiments demonstrate that POSPD consistently improves NAG models on four text generation tasks to a greater extent compared to knowledge distillation. This observation validates the necessity of exploring the alternatives for sequence-level knowledge distillation.

POS-Constrained Parallel Decoding for Non-autoregressive Generation

In order to deeply understand the capability of pretrained language models in text generation and conduct a diagnostic evaluation, we propose TGEA, an error-annotated dataset with multiple benchmark tasks for text generation from pretrained language models (PLMs). We use carefully selected prompt words to guide GPT-2 to generate candidate sentences, from which we select 47K for error annotation. Crowdsourced workers manually check each of these sentences and detect 12k erroneous sentences. We create an error taxonomy to cover 24 types of errors occurring in these erroneous sentences according to the nature of errors with respect to linguistics and knowledge (e.g., common sense). For each erroneous span in PLM-generated sentences, we also detect another span that is closely associated with it. Each error is hence manually labeled with comprehensive annotations, including the span of the error, the associated span, minimal correction to the error, the type of the error, and rationale behind the error. Apart from the fully annotated dataset, we also present a detailed description of the data collection procedure, statistics and analysis of the dataset. This is the first dataset with comprehensive annotations for PLM-generated texts, which facilitates the diagnostic evaluation of PLM-based text generation. Furthermore, we use TGEA as a benchmark dataset and propose a series of automatic diagnosis tasks, including error detection, error type classification, associated span detection, error rationale generation, to further promote future study on the automatic error detection and correction on texts generated by pretrained language models.

TGEA: An Error-Annotated Dataset and Benchmark Tasks for TextGeneration from Pretrained Language Models

This paper describes our systems for negation detection and time expression recognition in SemEval 2021 Task 10, Source-Free Domain Adaptation for Semantic Processing. We show that self-training, active learning and data augmentation techniques can improve the generalization ability of the model on the unlabeled target domain data without accessing source domain data. We also perform detailed ablation studies and error analyses for our time expression recognition systems to identify the source of the performance improvement and give constructive feedback on the temporal normalization annotation guidelines.

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DPR at SemEval-2021 Task 8: Dynamic Path Reasoning for Measurement Relation Extraction

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