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Recent research on the multi-head attention mechanism, especially that in pre-trained models such as BERT, has shown us heuristics and clues in analyzing various aspects of the mechanism. As most of the research focus on probing tasks or hidden states, previous works have found some primitive patterns of attention head behavior by heuristic analytical methods, but a more systematic analysis specific on the attention patterns still remains primitive. In this work, we clearly cluster the attention heatmaps into significantly different patterns through unsupervised clustering on top of a set of proposed features, which corroborates with previous observations. We further study their corresponding functions through analytical study. In addition, our proposed features can be used to explain and calibrate different attention heads in Transformer models.


COLING 2020

How Far Does BERT Look At: Distance-based Clustering and Analysis of BERT&#39;s Attention

multi-head attention

interpretability

transformer

stages 

COLING, the International Conference on Computational Linguistics, is one of the premier conferences for natural language processing and computational linguistics. Often grouped within the field of artificial intelligence, but actually pre-dating the development of artificial intelligence, advances in computational linguistics and natural language processing are now some of the major drivers behind the use of artificial intelligence for commercial and social applications – for example, on-line search, machine translation and with voice-assisted conversational devices.

First established in 1965, the biennial COLING conference is held in diverse parts of the globe and attracts participants from both top-ranked research centers and emerging countries. Today, the most important developments in our field are taking place not only in universities and academic research institutes, but also in industrial research departments and in technological startups. COLING conferences provide opportunities for all these communities to showcase their exciting developments.

COLING, the International Conference on Computational Linguistics, is one of the premier conferences for natural language processing and computational linguistics. Often grouped within the field of artificial intelligence, but actually pre-dating the development of artificial intelligence, advances in computational linguistics and natural language processing are now some of the major drivers behind the use of artificial intelligence for commercial and social applications – for example, on-line search, machine translation and with voice-assisted conversational devices.

How Far Does BERT Look At: Distance-based Clustering and Analysis of BERT's Attention

poster

We propose neural models that can normalize text by considering the similarities of word strings and sounds. We experimentally compared a model that considers the similarities of both word strings and sounds, a model that considers only the similarity of word strings or of sounds, and a model without the similarities as a baseline. Results showed that leveraging the word string similarity succeeded in dealing with misspellings and abbreviations, and taking into account the sound similarity succeeded in dealing with phonetic substitutions and emphasized characters. So that the proposed models achieved higher F1 scores than the baseline.

Neural text normalization leveraging similarities of strings and sounds

Children have less linguistic skills than adults, which makes it more difficult for them to understand some texts, for instance when browsing the Internet. In this context, we present a novel method which predicts the minimal age from which a text can be understood. This method analyses each sentence of a text using a recurrent neural network, and then aggregates this information to provide the text-level prediction. Different approaches are proposed and compared to baseline models, at sentence and text levels.
Experiments are carried out on a corpus of 1,500 texts and 160K sentences. Our best model, based on LSTMs, outperforms state-of-the-art results and achieves mean absolute errors of 1.86 and 2.28, at sentence and text levels, respectively.

Mama/Papa, Is this Text for Me?

Traditional word vectors, such as word2vec and glove, have a well-known inclination to conflate the semantic similarity with other semantic relations. As a result, the synonym and antonym relationships between words might not be well reflected in word embeddings. Namely, if two words are antonyms (synonyms), their corresponding word vectors could be very close (far) geometrically. Lots of downstream applications would be influenced by the property. For instance, if “good" and “bad" are close to each other, it would be hard to distinguish the sentiment polarity of a sentence. In this paper, we propose a new method called HRSWE. It has a competitive specialization performance compared with a strong baseline while runs dramatically faster and is more interpretable.

Specializing Word Vectors by Spectral Decomposition on Heterogeneously Twisted Graphs

Despite the advances in Natural Language Inference through the training of massive deep models, recent work has revealed the generalization difficulties of such models, which fail to perform on adversarial datasets with challenging linguistic phenomena. Such phenomena, however, can be handled well by symbolic systems. Thus, we propose Hy-NLI, a hybrid system that learns to identify an NLI pair as linguistically challenging or not. Based on that, it uses its symbolic or deep learning component, respectively, to make the final inference decision. We show how linguistically less complex cases are best solved by robust state-of-the-art models, like BERT and XLNet, while hard linguistic phenomena are best handled by our implemented symbolic engine. Our thorough evaluation shows that our hybrid system achieves state-of-the-art performance across mainstream and adversarial datasets and opens the way for further research into the hybrid direction.

Hy-NLI: a Hybrid system for Natural Language Inference

Automatic Language Identification (LI) or Dialect Identification (DI) of short texts of closely related languages or dialects, is one of the primary steps in many natural language processing pipelines. Language identification is considered a solved task in many cases; however, in the case of very closely related languages, or in an unsupervised scenario (where the languages are not known in advance), performance is still poor. In this paper, we propose the UnsupervisedDeep Language and Dialect Identification (UDLDI) method, which can simultaneously learn sentence embeddings and cluster assignments from short texts. The UDLDI model understands the sentence constructions of languages by applying attention to character relations which helps to optimize the clustering of languages. We have performed our experiments on three short-text datasets for different language families, each consisting of closely related languages or dialects, with very minimal training sets. Our experimental evaluations on these datasets have shown significant improvement over state-of-the-art unsupervised methods and our model has outperformed state-of-the-art LI and DI systems in supervised settings

Unsupervised Deep Language and Dialect Identification for Short Texts

In this study, we propose a beam search method to obtain diverse outputs in a local sequence transduction task where most of the tokens in the source and target sentences overlap, such as in grammatical error correction (GEC). In GEC, it is advisable to rewrite only the local sequences that must be rewritten while leaving the correct sequences unchanged. However, existing methods of acquiring various outputs focus on revising all tokens of a sentence. Therefore, existing methods may either generate ungrammatical sentences because they force the entire sentence to be changed or produce non-diversified sentences by weakening the constraints to avoid generating ungrammatical sentences. Considering these issues, we propose a method that does not rewrite all the tokens in a text, but only rewrites those parts that need to be diversely corrected. Our beam search method adjusts the search token in the beam according to the probability that the prediction is copied from the source sentence. The experimental results show that our proposed method generates more diverse corrections than existing methods without losing accuracy in the GEC task.

Generating Diverse Corrections with Local Beam Search \\ for Grammatical Error Correction

We propose a new few-shot text classification method. Compared with supervised
learning methods which require a large corpus of labeled documents, our method aims to make it possible to classify unlabeled text with few labeled data. To achieve this goal, we take advantage of advanced pre-trained language model to extract the semantic features of each document. Furthermore, we utilize an edge-labeling graph neural network to implicitly models the intra-cluster similarity and the inter-cluster dissimilarity of the documents. Finally, we take the results of the graph neural network as the input of a prototypical network to classify the unlabeled texts. We verify the effectiveness of our method on a sentiment analysis dataset and a relation classification dataset and achieve the state-of-the-art performance on both tasks.

Few-Shot Text Classification with Edge-Labeling Graph NeuralNetwork-Based Prototypical Network

Morphological analysis and disambiguation is crucial for natural language processing of morphologically rich languages. Enormous effort is required to collect accurate morphological parses in low resource settings such as the case of Kinyarwanda verbal forms. In this work, we propose and evaluate a method for morphological disambiguation of Kinyarwanda verbal forms using inflection:stem pairs provided by human annotators through a crowd-sourcing effort. We use two level morphological analysis, feature engineering and a maximum entropy classifier to effectively disambiguate morphological segmentations produced by the analyzer, achieving 89% accuracy without phrasal context.

Morphological Disambiguation from Stemming Data

As a conversational intelligence task, visual dialog entails answering a series of questions grounded in an image, using the dialog history as context. To generate correct answers, the comprehension of the semantic dependencies among implicit visual and textual contents is critical.

Visual-Textual Alignment for Graph Inference in Visual Dialog

Relation Classification (RC) plays an important role in natural language processing (NLP). Current conventional supervised and distantly supervised RC models always make a closed-world assumption which ignores the emergence of novel relations in an open environment. To incrementally recognize the novel relations, current two solutions (i.e, re-training and lifelong learning) are designed but suffer from the lack of large-scale labeled data for novel relations. Meanwhile, prototypical network enjoys better performance on both fields of deep supervised learning and few-shot learning. However, it still suffers from the incompatible feature embedding problem when the novel relations come in. Motivated by them, we propose a two-phase
prototypical network with prototype attention alignment and triplet loss to dynamically recognize the novel relations with a few support instances meanwhile without catastrophic forgetting. Extensive experiments are conducted to evaluate the effectiveness of our proposed model.

How Far Does BERT Look At: Distance-based Clustering and Analysis of BERT's Attention

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How Far Does BERT Look At: Distance-based Clustering and Analysis of BERT's Attention

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Neural text normalization leveraging similarities of strings and sounds

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Rethinking the Value of Transformer Components

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