Dominican Republic

Relating entities and events in text is a key component of natural language understanding. Cross-document coreference resolution, in particular, is important for the growing interest in multi-document analysis tasks. In this work we propose a new model that extends the efficient sequential prediction paradigm for coreference resolution to cross-document settings and achieves competitive results for both entity and event coreference while providing strong evidence of the efficacy of both sequential models and higher-order inference in cross-document settings. Our model incrementally composes mentions into cluster representations and predicts links between a mention and the already constructed clusters, approximating a higher-order model. In addition, we conduct extensive ablation studies that provide new insights into the importance of various inputs and representation types in coreference.

EMNLP 2021

Sequential Cross-Document Coreference Resolution

EMNLP 2021 is planned to be a hybrid event in Punta Cana, Dominican Republic, with both on-site and fully virtual participation possible. The experience for on-site participants would closely approximate a normal pre-COVID *ACL conference, with 5-6 thematically organized parallel sessions and live Q/A and interactive discussion immediately after the talks. Presentations by virtual participants will be equitably interleaved with those of on-site participants, projected on the auditorium screens as if on-site, and also followed immediately by live Q/A and interactive discussion at a time during reasonable waking hours for the virtual presenter. For all participants, on-site and virtual, who are unable to attend a session due to either time-zone issues or because they are participating in another session live, talk recordings and slides will be available online at a minimum after the live presentation (and in many cases before as well), and questions may be submitted in advance on session-specific discussion boards and answered live in session with the usual visual aids if desired.

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EMNLP 2021 is planned to be a hybrid event in Punta Cana, Dominican Republic, with both on-site and fully virtual participation possible.

poster

Measuring event salience is essential in the understanding of stories. This paper takes a recent unsupervised method for salience detection derived from Barthes Cardinal Functions and theories of surprise and applies it to longer narrative forms. We improve the standard transformer language model by incorporating an external knowledgebase (derived from Retrieval Augmented Generation) and adding a memory mechanism to enhance performance on longer works. We use a novel approach to derive salience annotation using chapter-aligned summaries from the Shmoop corpus for classic literary works. Our evaluation against this data demonstrates that our salience detection model improves performance over and above a non-knowledgebase and memory augmented language model, both of which are crucial to this improvement.

Memory and Knowledge Augmented Language Models for Inferring Salience in Long-Form Stories

This paper proposes to study a ﬁne-grained semantic novelty detection task, which can be illustrated with the following example. It is normal that a person walks a dog in the park, but if someone says "A man is walking a chicken in the park," it is novel. Given a set of natural language descriptions of normal scenes, we want to identify descriptions of novel scenes. We are not aware of any existing work that solves the problem. Although existing novelty or anomaly detection algorithms are applicable, since they are usually topic-based, they perform poorly on our ﬁne-grained semantic novelty detection task. This paper proposes an effective model (called GAT-MA) to solve the problem and also contributes a new dataset. Experimental evaluation shows that GAT-MA outperforms 11 baselines by large margins.

Semantic Novelty Detection in Natural Language Descriptions

Previous work on crosslingual Relation and Event Extraction (REE) suffers from the monolingual bias issue due to the training of models on only the source language data. An approach to overcome this issue is to use unlabeled data in the target language to aid the alignment of crosslingual representations, i.e., via fooling a language discriminator. However, as this approach does not condition on class information, a target language example of a class could be incorrectly aligned to a source language example of a different class. To address this issue, we propose a novel crosslingual alignment method that leverages class information of REE tasks for representation learning. In particular, we propose to learn two versions of representation vectors for each class in an REE task based on either source or target language examples. Representation vectors for corresponding classes will then be aligned to achieve class-aware alignment for crosslingual representations. In addition, we propose to further align representation vectors for languageuniversal word categories (i.e., parts of speech and dependency relations). As such, a novel filtering mechanism is presented to facilitate the learning of word category representations from contextualized representations on input texts based on adversarial learning. We conduct extensive crosslingual experiments with English, Chinese, and Arabic over REE tasks. The results demonstrate the benefits of the proposed method that significantly advances the state-of-the-art performance in these settings.

Crosslingual Transfer Learning for Relation and Event Extraction via Word Category and Class Alignments

Recent transformer-based approaches demonstrate promising results on relational scientific information extraction. Existing datasets focus on high-level description of how research is carried out. Instead we focus on the subtleties of how experimental associations are presented by building SciClaim, a dataset of scientific claims drawn from Social and Behavior Science (SBS), PubMed, and CORD-19 papers. Our novel graph annotation schema incorporates not only coarse-grained entity spans as nodes and relations as edges between them, but also fine-grained attributes that modify entities and their relations, for a total of 12,738 labels in the corpus. By including more label types and more than twice the label density of previous datasets, SciClaim captures causal, comparative, predictive, statistical, and proportional associations over experimental variables along with their qualifications, subtypes, and evidence. We extend work in transformer-based joint entity and relation extraction to effectively infer our schema, showing the promise of fine-grained knowledge graphs in scientific claims and beyond.

Extracting Fine-Grained Knowledge Graphs of Scientific Claims: Dataset and Transformer-Based Results

We introduce SelfExplain, a novel self-explaining model that explains a text classifier's predictions using phrase-based concepts. SelfExplain augments existing neural classifiers by adding (1) a globally interpretable layer that identifies the most influential concepts in the training set for a given sample and (2) a locally interpretable layer that quantifies the contribution of each local input concept by computing a relevance score relative to the predicted label. Experiments across five text-classification datasets show that SelfExplain facilitates interpretability without sacrificing performance. Most importantly, explanations from SelfExplain show sufficiency for model predictions and are perceived as adequate, trustworthy and understandable by human judges compared to existing widely-used baselines.

SelfExplain: A Self-Explaining Architecture for Neural Text Classifiers

This paper presents an empirical study to efficiently build named entity recognition (NER) systems when a small amount of in-domain labeled data is available. Based upon recent Transformer-based self-supervised pre-trained language models (PLMs), we investigate three orthogonal schemes to improve model generalization ability in few-shot settings: (1) metalearning to construct prototypes for different entity types, (2) task-specific supervised pretraining on noisy web data to extract entityrelated representations and (3) self-training to leverage unlabeled in-domain data. On 10 public NER datasets, we perform extensive empirical comparisons over the proposed schemes and their combinations with various proportions of labeled data, our experiments show that (i) in the few-shot learning setting, the proposed NER schemes significantly improve or outperform the commonly used baseline, a PLM-based linear classifier fine-tuned using domain labels. (ii) We create new state-of-theart results on both few-shot and training-free settings compared with existing methods.

Few-Shot Named Entity Recognition: An Empirical Baseline Study

We present the first use of pre-trained GPT-2 and T5 models for elementary level math word problems(MWPs) generation. we fine-tuned these models with two datasets namely simple MWPs and algebraic MWPs. we obtained the results which are better than state of art.

Elementary-Level Math Word Problem Generation using Pre-Trained Transformers

The identification of child predators in chat rooms has been a concerning issue for a long time. The COVID-19 pandemic has caused an increase in internet usage and thus, an increase in the incidents of child exploitation. Hence, there is an urgent requirement to develop a system that can protect children from sexual exploitation. In this work, we propose a BERT-based approach to the early detection of child predators using the PAN'12 dataset. Our methodology makes use of a minimum number of dialogues and is able to identify child predators in chats within 45 minutes. An extensive comparison against other deep learning methods suited to text classification tasks and a detailed qualitative analysis makes the case for our proposed methodology.

Towards the Early Detection of Child Predators in Chat Rooms: A BERT-based Approach

Current machine translation methods struggle to translate phrases that contain words that appear infrequently in the training data. Low-resource languages in particular are harmed by this because they have such little data that rare words may be seen only once. This issue can be partly addressed using approaches that explicitly incorporate word-level translation lexicons (Akyürek and Andreas, 2021). However, existing approaches for constructing these lexicons require either substantial human annotation or large datasets. We propose a model for few-shot learning of lexical translation rules. Our approach enables neural machine translation models to efficiently learn translations of infrequent words. In experiments on low-resource English to Spanish and English to Tamil translation, our approach yields BLEU improvements on sentences containing words seen only once during training, resulting in increased performance as a whole.

One-Shot Lexicon Learning for Low-Resource Machine Translation

The presentation includes a brief introduction with providing descriptive details about the confusing points contained in the research paper

Sequential Cross-Document Coreference Resolution

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Memory and Knowledge Augmented Language Models for Inferring Salience in Long-Form Stories

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