By how much do they outpeform existing text denoising models?
Answer with content missing: (Table 4) Mean reciprocal rank of proposed model is 0.76 compared to 0.64 of bag-of-ngrams.
We introduce the first generic text representation model that is completely nonsymbolic, i.e., it does not require the availability of a segmentation or tokenization method that attempts to identify words or other symbolic units in text. This applies to training the parameters of the model on a training corpus as well as to applying it when computing the representation of a new text. We show that our model performs better than prior work on an information extraction and a text denoising task.
Character-level models can be grouped into three classes. (i) End-to-end models learn a separate model on the raw character (or byte) input for each task; these models estimate task-specific parameters, but no representation of text that would be usable across tasks is computed. Throughout this paper, we refer to INLINEFORM0 as the “representation” of INLINEFORM1 only if INLINEFORM2 is a generic rendering of INLINEFORM3 that can be used in a general way, e.g., across tasks and domains. The activation pattern of a hidden layer for a given input sentence in a multilayer perceptron (MLP) is not a representation according to this definition if it is not used outside of the MLP. (ii) Character-level models of words derive a representation of a word INLINEFORM4 from the character string of INLINEFORM5 , but they are symbolic in that they need text segmented into tokens as input. (iii) Bag-of-character-ngram models, bag-of-ngram models for short, use character ngrams to encode sequence-of-character information, but sequence-of-ngram information is lost in the representations they produce.[0]A short version of this paper appears as BIBREF0 . Our premise is that text representations are needed in NLP. A large body of work on word embeddings demonstrates that a generic text representation, trained in an unsupervised fashion on large corpora, is useful. Thus, we take the view that group (i) models, end-to-end learning without any representation learning, is not a good general approach for NLP. We distinguish training and utilization of the text representation model. We use “training” to refer to the method by which the model is learned and “utilization” to refer to the application of the model to a piece of text to compute a representation of the text. In many text representation models, utilization is trivial. For example, for word embedding models, utilization amounts to a simple lookup of a word to get its precomputed embedding. However, for the models we consider, utilization is not trivial and we will discuss different approaches. Both training and utilization can be either symbolic or nonsymbolic. We define a symbolic approach as one that is based on tokenization, i.e., a segmentation of the text into tokens. Symbol identifiers (i.e., tokens) can have internal structure – a tokenizer may recognize tokens like “to and fro” and “London-based” that contain delimiters – and may be morphologically analyzed downstream. We define a nonsymbolic approach as one that is tokenization-free, i.e., no assumption is made that there are segmentation boundaries and that each segment (e.g., a word) should be represented (e.g., by a word embedding) in a way that is independent of the representations (e.g., word embeddings) of neighboring segments. Methods for training text representation models that require tokenized text include word embedding models like word2vec BIBREF1 and most group (ii) methods, i.e., character-level models like fastText skipgram BIBREF2 . Bag-of-ngram models, group (iii) models, are text representation utilization models that typically compute the representation of a text as the sum of the embeddings of all character ngrams occurring in it, e.g., WordSpace BIBREF3 and CHARAGRAM BIBREF4 . WordSpace and CHARAGRAM are examples of mixed training-utilization models: training is performed on tokenized text (words and phrases), utilization is nonsymbolic. We make two contributions in this paper. (i) We propose the first generic method for training text representation models without the need for tokenization and address the challenging sparseness issues that make this difficult. (ii) We propose the first nonsymbolic utilization method that fully represents sequence information – in contrast to utilization methods like bag-of-ngrams that discard sequence information that is not directly encoded in the character ngrams themselves.