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Dataless text classification is capable of classifying documents into previously unseen labels by assigning a score to any document paired with a label description. While promising, it crucially relies on accurate descriptions of the label set for each downstream task. This reliance causes dataless classifiers to be highly sensitive to the choice of label descriptions and hinders the broader application of dataless classification in practice. In this paper, we ask the following question: how can we improve dataless text classification using the inputs of the downstream task dataset? Our primary solution is a clustering based approach. Given a dataless classifier, our approach refines its set of predictions using k-means clustering. We demonstrate the broad applicability of our approach by improving the performance of two widely used classifier architectures, one that encodes text-category pairs with two independent encoders and one with a single joint encoder. Experiments show that our approach consistently improves dataless classification across different datasets and makes the classifier more robust to the choice of label descriptions.

ACL 2021

Unsupervised Label Refinement Improves Dataless Text Classification

unsupervised text classification

**Welcome to ACL-IJCNLP 2021!**

The great event is jointly organized by the Association for Computational Linguistics (ACL) and Asian Federation of Natural Language Processing (AFNLP). 

This Joint Conference of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (ACL-IJCNLP 2021) was to be held in Bangkok, Thailand, during August 1-6, 2021 and is now virtual on the Underline platform.

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workshop paper

Pre-trained language models (LMs) are currently integral to many natural language processing systems. Although multilingual LMs were also introduced to serve many languages, these have limitations such as being costly at inference time and the size and diversity of non-English data involved in their pre-training. We remedy these issues for a collection of diverse Arabic varieties by introducing two powerful deep bidirectional transformer-based models, ARBERT and MARBERT. To evaluate our models, we also introduce ARLUE, a new benchmark for multi-dialectal Arabic language understanding evaluation. ARLUE is built using 42 datasets targeting six different task clusters, allowing us to offer a series of standardized experiments under rich conditions. When fine-tuned on ARLUE, our models collectively achieve new state-of-the-art results across the majority of tasks (37 out of 48 classification tasks, on the 42 datasets). Our best model acquires the highest ARLUE score (77.40) across all six task clusters, outperforming all other models including XLM-R Large (~ 3.4x larger size). Our models are publicly available at https://github.com/UBC-NLP/marbert and ARLUE will be released through the same repository.

ARBERT & MARBERT: Deep Bidirectional Transformers for Arabic

In this work, we introduce a corpus for satire detection in Romanian news. We gathered 55,608 public news articles from multiple real and satirical news sources, composing one of the largest corpora for satire detection regardless of language and the only one for the Romanian language. We provide an official split of the text samples, such that training news articles belong to different sources than test news articles, thus ensuring that models do not achieve high performance simply due to overfitting. We conduct experiments with two state-of-the-art deep neural models, resulting in a set of strong baselines for our novel corpus. Our results show that the machine-level accuracy for satire detection in Romanian is quite low (under 73% on the test set) compared to the human-level accuracy (87%), leaving enough room for improvement in future research.

SaRoCo: Detecting Satire in a Novel Romanian Corpus of News Articles

Faceted summarization provides briefings of a document from different perspectives. Readers can quickly comprehend the main points of a long document with the help of a structured outline. However, little research has been conducted on this subject, partially due to the lack of large-scale faceted summarization datasets. In this study, we present FacetSum, a faceted summarization benchmark built on Emerald journal articles, covering a diverse range of domains. Different from traditional document-summary pairs, FacetSum provides multiple summaries, each targeted at specific sections of a long document, including the purpose, method, findings, and value. Analyses and empirical results on our dataset reveal the importance of bringing structure into summaries. We believe FacetSum will spur further advances in summarization research and foster the development of NLP systems that can leverage the structured information in both long texts and summaries.

Bringing Structure into Summaries: a Faceted Summarization Dataset for Long Scientific Documents

Recent investigations into the inner-workings of state-of-the-art large-scale pre-trained Transformer-based Natural Language Understanding (NLU) models indicate that they appear to understand human-like syntax, at least to some extent. We provide novel evidence that complicates this claim: we find that state-of-the-art Natural Language Inference (NLI) models assign the same labels to permuted examples as they do to the original, i.e. they are invariant to random word-order permutations. This behavior notably differs from that of humans; we struggle to understand the meaning of ungrammatical sentences. To measure the severity of this issue, we propose a suite of metrics and investigate which properties of particular permutations lead models to be word order invariant. For example, in MNLI dataset we find almost all (98.7%) examples contain at least one permutation which elicits the gold label. Models are even able to assign gold labels to permutations that they originally failed to predict correctly. We provide a comprehensive empirical evaluation of this phenomenon, and further show that this issue exists in pre-Transformer RNN / ConvNet based encoders, as well as across multiple languages (English and Chinese). Our code and data are available at https://github.com/facebookresearch/unlu.

UnNatural Language Inference

We investigate the semantic knowledge of language models (LMs), focusing on (1) whether these LMs create categories of linguistic environments based on their semantic monotonicity properties, and (2) whether these categories play a similar role in LMs as in human language understanding, using negative polarity item licensing as a case study. 
We introduce a series of experiments consisting of probing with diagnostic classifiers (DCs), linguistic acceptability tasks, as well as a novel DC ranking method that tightly connects the probing results to the inner workings of the LM.
By applying our experimental pipeline to LMs trained on various filtered corpora, we are able to gain stronger insights into the semantic generalizations that are acquired by these models.

Language Models Use Monotonicity to Assess NPI Licensing

Cross-domain slot filling focuses on using la- beled data from source domains to train a slot filling model for target domains. It is of great significance for transferring a dialogue system into new domains. Most of the existing work focused on building a cross-domain transfer model. From the perspective of slots them- selves, this paper proposes a model-agnostic Slot Transferability Measure (STM) for evalu- ating the transferability from a source slot to a target slot, specifically, the degree that labeled data of the source slot is helpful to train the slot filling model for the target slot. We also give a STM-based method for a model to select helpful source slots and their labeled data for a given target slot. Experimental results on mul- tiple existing models and datasets show that our method significantly outperforms state-of- the-art baselines in cross-domain slot filling. The code is available at https://github. com/luhengtong/STM-for-cdsf.git.

Slot Transferability for Cross-domain Slot Filling

The attention-based encoder-decoder framework is widely used in many natural language generation tasks. The atten3 tion mechanism builds alignments between target words and source items that facilitate text generation. Previous work proposes supervised attention that uses human knowledge to guide the attention mechanism to learn better alignments. However, well designed supervision built from ideal align8 ments can be costly or even infeasible. In this paper, we build a Generalized Supervised Attention method (GSA) based on quasi alignments, which specify candidate sets of alignments and are much easier to obtain than ideal alignments. We design a Summation Cross-Entropy (SCE) loss and a Supervised Multiple Attention (SMA) structure to accommodate quasi alignments. Experiments on three text generation tasks demonstrate that GSA improves generation performance and is robust against errors in attention supervision.

Generalized Supervised Attention for Text Generation

Recent papers have introduced methods to incorporate gazetteer features and entity segmentation techniques in neural named entity recognition models. These papers rely on different resources and include features not related to the use of gazetteers, rendering impossible the comparison of the relative effectiveness of the approaches. Here, we provide a comprehensive overview of methods for incorporating gazetteers and for entity segmentation. We evaluate representative methods from each in similar settings for a fair comparison and identify the ones that are consistently better across datasets and input representations. We further show that gazetteers improve entity segmentation and not just entity typing. Hence, we explore their utility in recognizing long entities, a problem for which entity segmentation techniques were developed. Our work explains the mechanisms via which gazetteers improve the performance of neural NER models.

The Utility and Interplay of Gazetteers and Entity Segmentation for Named Entity Recognition in English

We introduce a language modeling architecture which operates over sequences of images, or over multimodal sequences of images with associated labels. We use this architecture alongside other embedding models to investigate a category of signs called complex graphemes (CGs) in the undeciphered proto-Elamite script. We argue that CGs have meanings which are at least partly compositional, and we discover novel rules governing the construction of CGs. We find that a language model over sign images produces more interpretable results than a model over text or over sign images and text, which suggests that the names given to signs may be obscuring signals in the corpus. Our results reveal previously unknown regularities in proto-Elamite sign use that can inform future decipherment efforts, and our image-aware language model provides a novel way to abstract away from biases introduced by human annotators.

Compositionality of Complex Graphemes in the Undeciphered Proto-Elamite Script using Image and Text Embedding Models

How much information do NLP tasks really need from a transformer's attention mechanism at application-time (inference)? From recent work, we know that there is sparsity in transformers and that the floating-points within its computation can be discretized to fewer values with minimal loss to task accuracies. However, this requires retraining or even creating entirely new models, both of which can be expensive and carbon-emitting. Focused on optimizations that do not require training, we systematically study the full range of typical attention values necessary.
This informs the design of an inference-time quantization technique using both pruning and log-scaled mapping which produces only a few (e.g. 2^3) unique values. Over the tasks of question answering and sentiment analysis, we find nearly 80% of attention values can be pruned to zeros with minimal (< 1.0%) relative loss in accuracy. We use this pruning technique in conjunction with quantizing the attention values to only a 3-bit format, without retraining, resulting in only a 0.8% accuracy reduction on question answering with fine-tuned RoBERTa.

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Unsupervised Label Refinement Improves Dataless Text Classification

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