Dominican Republic

This paper contributes to the thread of research on the learnability of different dependency annotation schemes: one (‘semantic’) favouring content words as heads of dependency relations and the other (‘syntactic’) favouring syntactic heads. Several studies have lent support to the idea that choosing syntactic criteria for assigning heads in dependency trees improves the performance of dependency parsers. This may be explained by postulating that syntactic approaches are generally more learnable. In this study, we test this hypothesis by comparing the performance of five parsing systems (both transition- and graph-based) on a selection of 21 treebanks, each in a ‘semantic’ variant, represented by standard UD (Universal Dependencies), and a ‘syntactic’ variant, represented by SUD (Surface-syntactic Universal Dependencies): unlike previously reported experiments, which considered learnability of ‘semantic’ and ‘syntactic’ annotations of particular constructions in vitro, the experiments reported here consider whole annotation schemes in vivo. Additionally, we compare these annotation schemes using a range of quantitative syntactic properties, which may also reflect their learnability. The results of the experiments show that SUD tends to be more learnable than UD, but the advantage of one or the other scheme depends on the parser and the
corpus in question.

EMNLP 2021

Comparing learnability of two dependency schemes: ‘semantic’ (UD) and ‘syntactic’ (SUD)

The purpose of the AAAI conference series is to promote research in Artificial Intelligence (AI) and foster scientific exchange between researchers, practitioners, scientists, students, and engineers across the entirety of AI and its affiliated disciplines. AAAI-23 is the Thirty-Seventh AAAI Conference on Artificial Intelligence. The theme of this conference is to create collaborative bridges within and beyond AI. Like previous AAAI conferences, AAAI-23 will feature technical paper presentations, special tracks, invited speakers, workshops, tutorials, poster sessions, senior member presentations, competitions, and exhibit programs, and two new activities: a Bridge Program and a Lab Program. Many of these activities are tailored to the theme of bridges and all are selected according to the highest standards, with additional programs for students and young researchers.<br><br><br>
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AAAI 2023

The purpose of the AAAI conference series is to promote research in Artificial Intelligence (AI) and foster scientific exchange between researchers, practitioners, scientists, students, and engineers across the entirety of AI and its affiliated disciplines.

technical paper

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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.

While large language models a la BERT are used ubiquitously in NLP, pretraining them is considered a luxury that only a few well-funded industry labs can afford. How can one train such models with a more modest budget? We present a recipe for pretraining a masked language model in 24 hours using a single low-end deep learning server. We demonstrate that through a combination of software optimizations, design choices, and hyperparameter tuning, it is possible to produce models that are competitive with BERT-base on GLUE tasks at a fraction of the original pretraining cost.

How to Train BERT with an Academic Budget

Coordination is a phenomenon of language that conjoins two or more terms or phrases using a coordinating conjunction. Although coordination has been explored extensively in the linguistics literature, the rules and constraints that govern its structure are still largely elusive and widely debated amongst linguists. This paper presents a study of two-termed unlike coordinations in particular, where the two conjuncts of the coordination phrase form valid constituents but have distinct categories. We conducted a syntactic analysis of the phrasal categories that can be conjoined in such unlike coordinations through a computational corpus-based approach, utilizing the Corpus of Contemporary American English (COCA) as the main data source, as well as the Penn Treebank (PTB). The results show that the two conjuncts within unlike coordinations display different properties based on their position, supporting an antisymmetric view of the structure of coordination. This research provides new data and perspectives through the use of statistical techniques that can help shape future theories and models of coordination.

A Corpus-based Syntactic Analysis of Two-termed Unlike Coordination

Deriving and modifying graphs from natural language text has become a versatile basis technology for information extraction with applications in many subfields, such as semantic parsing or knowledge graph construction. A recent work used this technique for modifying scene graphs (He et al., 2020), by first encoding the original graph and then generating the modified one based on this encoding. In this work, we show that we can considerably increase performance on this problem by phrasing it as graph extension instead of graph generation. We propose the first model for the resulting graph extension problem based on autoregressive sequence labelling. On three scene graph modification data sets, this formulation leads to improvements in accuracy over the state-of-the-art between 13 and 26 percentage points. Furthermore, we introduce a novel data set from the biomedical domain which has much larger linguistic variability and more complex graphs than the scene graph modification data sets. For this data set, the state of the art fails to generalize, while our model can produce meaningful predictions.

Extend, don’t rebuild: Phrasing conditional graph modification as autoregressive sequence labelling

Pretrained Language Models (PTLMs) yield state-of-the-art performance on many Natural Language Processing tasks, including syntax, semantics and commonsense reasoning. In this paper, we focus on identifying to what extent do PTLMs capture semantic attributes and their values, e.g. the relation between rich and high net worth. We use PTLMs to predict masked tokens using patterns and lists of items from Wikidata in order to verify how likely PTLMs encode semantic attributes along with their values. Such inferences based on seman- tics are intuitive for us humans as part of our language understanding. Since PTLMs are trained on large amount of Wikipedia data, we would assume that they can generate similar predictions. However, our findings reveal that PTLMs perform still much worse than humans on this task. We show an analysis which ex- plains how to exploit our methodology to inte- grate better context and semantics into PTLMs using knowledge bases.

Probing Pre-trained Language Models for Semantic Attributes and their Values

This paper investigates whether the power of the models pre-trained on text data, such as BERT, can be transferred to general token sequence classification applications. To verify pre-trained models' transferability, we test the pre-trained models on text classification tasks with meanings of tokens mismatches, and real-world non-text token sequence classification data, including amino acid, DNA, and music. We find that even on non-text data, the models pre-trained on text converge faster, perform better than the randomly initialized models, and only slightly worse than the models using task-specific knowledge. We also find that the representations of the text and non-text pre-trained models share non-trivial similarities.

Is BERT a Cross-Disciplinary Knowledge Learner? A Surprising Finding of Pre-trained Models’ Transferability

Understanding when a text snippet does not provide a sought after information is an essential part of natural language understanding. Recent work (SQuAD 2.0, Rajpurkar et al., 2018) has attempted to make some progress in this direction by enriching the SQuAD dataset for the Extractive QA task with unanswerable
questions. However, as we show, the performance of a top system trained on SQuAD 2.0 drops considerably in out-of-domain scenarios, limiting its use in practical situations. In order to study this we build an out-of-domain corpus, focusing on simple event-based questions and distinguish between two types of IDK questions: competitive questions, where the context includes an entity of the same
type as the expected answer, and simpler, non-competitive questions where there is no entity of the same type in the context. We find that SQuAD 2.0-based models fail even in the case of the simpler questions. We then analyze the similarities and differences between the IDK phenomenon in Extractive QA and the Recognizing Textual Entailments task (RTE, Dagan et al., 2013) and investigate the extent to which the latter can be used to improve the performance.

Do We Know What We Don’t Know? Studying Unanswerable Questions beyond SQuAD 2.0

Pre-trained language models (PLMs) like BERT have made great progress in NLP. News articles usually contain rich textual information, and PLMs have the potentials to enhance news text modeling for various intelligent news applications like news recommendation and retrieval. However, most existing PLMs are in huge size with hundreds of millions of parameters. Many online news applications need to serve millions of users with low latency, which poses great challenge to incorporating PLMs in these scenarios. Knowledge distillation techniques can compress a large PLM into a much smaller one and meanwhile keep good performance. However, existing language models are pre-trained and distilled on general corpus like Wikipedia, which have gaps with the news domain and may be suboptimal for news intelligence. In this paper, we propose NewsBERT, which can distill PLMs for efficient and effective news intelligence. In our approach, we design a teacher-student joint learning and distillation framework to collaboratively learn both teacher and student models, where the student model can learn from the learning experience of the teacher model. In addition, we propose a momentum distillation method by incorporating the gradients of teacher model into the update of student model to better transfer the knowledge learned by the teacher model. Thorough experiments on two real-world datasets with three tasks show that NewsBERT can empower various intelligent news applications with much smaller models.

NewsBERT: Distilling Pre-trained Language Model for Intelligent News Application

To alleviate human efforts from obtaining large-scale annotations, Semi-Supervised Relation Extraction methods aim to leverage unlabeled data in addition to learning from limited samples. Existing self-training methods suffer from the gradual drift problem, where noisy pseudo labels on unlabeled data are incorporated during training. To alleviate the noise in pseudo labels, we propose a method called MetaSRE, where a Relation Label Generation Network generates quality assessment on pseudo labels by (meta) learning from the successful and failed attempts on Relation Classification Network as an additional meta-objective. To reduce the influence of noisy pseudo labels, MetaSRE adopts a pseudo label selection and exploitation scheme which assesses pseudo label quality on unlabeled samples and only exploits high-quality pseudo labels in a self-training fashion to incrementally augment labeled samples for both robustness and accuracy. Experimental results on two public datasets demonstrate the effectiveness of the proposed approach.

Semi-supervised Relation Extraction via Incremental Meta Self-Training

This study investigates the applicability of semantic change detection methods in descriptively oriented linguistic research. It specifically focuses on contact-induced semantic shifts in Quebec English. We contrast synchronic data from different regions in order to identify the meanings that are specific to Quebec and potentially related to language contact. Type-level embeddings are used to detect new semantic shifts, and token-level embeddings to isolate regionally specific occurrences. We introduce a new 80-item test set and conduct both quantitative and qualitative evaluations.
We demonstrate that diachronic word embedding methods can be applied to contactinduced semantic shifts observed in synchrony, obtaining results comparable to the state of the art on similar tasks in diachrony. However, we show that encouraging evaluation results do not translate to practical value in detecting new semantic shifts. Finally, our application of token-level embeddings accelerates manual data exploration and provides an efficient way of scaling up sociolinguistic analyses.

Detecting Contact-Induced Semantic Shifts: What Can Embedding-Based Methods Do in Practice?

In this paper we present SPRING Online Services, a Web interface and RESTful APIs
for our state-of-the-art AMR parsing and generation system, SPRING (Symmetric PaRsIng aNd Generation). 
The Web interface has been developed to be easily used by the Natural Language Processing community, as well as by the general public. 
It provides, among other things, a highly interactive visualization platform and a feedback mechanism to obtain user suggestions for further improvements of the system's output. 
Moreover, our RESTful APIs enable easy integration of SPRING in downstream applications where AMR structures are needed.
Finally, we make SPRING Online Services freely available at http://nlp.uniroma1.it/spring and, in addition, we release extra model checkpoints to be used with the original SPRING Python code.

Comparing learnability of two dependency schemes: ‘semantic’ (UD) and ‘syntactic’ (SUD)

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Comparing learnability of two dependency schemes: ‘semantic’ (UD) and ‘syntactic’ (SUD)

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How to Train BERT with an Academic Budget

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AMOM: Adaptive Masking over Masking for Conditional Masked Language Model

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