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"date_generated": "2023-01-19T09:08:09.887561Z"
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"title": "MultiGranCNN: An Architecture for General Matching of Text Chunks on Multiple Levels of Granularity",
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"first": "Wenpeng",
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"last": "Yin",
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"institution": "University of Munich",
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"email": "wenpeng@cis.uni-muenchen.de"
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{
"first": "Hinrich",
"middle": [],
"last": "Sch\u00fctze",
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"institution": "University of Munich",
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"abstract": "We present MultiGranCNN, a general deep learning architecture for matching text chunks. MultiGranCNN supports multigranular comparability of representations: shorter sequences in one chunk can be directly compared to longer sequences in the other chunk. Multi-GranCNN also contains a flexible and modularized match feature component that is easily adaptable to different types of chunk matching. We demonstrate stateof-the-art performance of MultiGranCNN on clause coherence and paraphrase identification tasks.",
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"text": "We present MultiGranCNN, a general deep learning architecture for matching text chunks. MultiGranCNN supports multigranular comparability of representations: shorter sequences in one chunk can be directly compared to longer sequences in the other chunk. Multi-GranCNN also contains a flexible and modularized match feature component that is easily adaptable to different types of chunk matching. We demonstrate stateof-the-art performance of MultiGranCNN on clause coherence and paraphrase identification tasks.",
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"text": "Many natural language processing (NLP) tasks can be posed as classifying the relationship between two TEXTCHUNKS (cf. , Bordes et al. (2014b) ) where a TEXTCHUNK can be a sentence, a clause, a paragraph or any other sequence of words that forms a unit.",
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"section": "Introduction",
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"text": "Paraphrasing (Figure 1 , top) is one task that we address in this paper and that can be formalized as classifying a TEXTCHUNK relation. The two classes correspond to the sentences being (e.g., the pair <p, q + >) or not being (e.g., the pair <p, q \u2212 >) paraphrases of each other. Another task we look at is clause coherence (Figure 1 , bottom). Here the two TEXTCHUNK relation classes correspond to the second clause being (e.g., the pair <x, y + >) or not being (e.g., the pair <x, y \u2212 >) a discourse-coherent continuation of the first clause. Other tasks that can be formalized as TEXTCHUNK relations are question answering (QA) (is the second chunk an answer to the first?), textual inference (does the first chunk imply the second?) and machine translation (are the two chunks translations of each other?). p PDC will also almost certainly fan the flames of speculation about Longhorn's release.",
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"text": "(Figure 1",
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"text": "q + PDC will also almost certainly reignite speculation about release dates of Microsoft 's new products. q \u2212 PDC is indifferent to the release of Longhorn.",
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"text": "x The dollar suffered its worst one-day loss in a month, y + falling to 1.7717 marks . . . from 1.7925 marks yesterday. y \u2212 up from 112.78 yen in late New York trading yesterday. In this paper, we present MultiGranCNN, a general architecture for TEXTCHUNK relation classification. MultiGranCNN can be applied to a broad range of different TEXTCHUNK relations. This is a challenge because natural language has a complex structure -both sequential and hierarchicaland because this structure is usually not parallel in the two chunks that must be matched, further increasing the difficulty of the task. A successful detection algorithm therefore needs to capture not only the internal structure of TEXTCHUNKS, but also the rich pattern of their interactions.",
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"text": "MultiGranCNN is based on two innovations that are critical for successful TEXTCHUNK relation classification. First, the architecture is designed to ensure multigranular comparability. For general matching, we need the ability to match short sequences in one chunk with long sequences in the other chunk. For example, what is expressed by a single word in one chunk (\"reignite\" in q + in the figure) may be expressed by a sequence of several words in its paraphrase (\"fan the flames of\" in p). To meet this objective, we learn representations for words, phrases and the entire sentence that are all mutually comparable; in particular, these representations all have the same dimensionality and live in the same space.",
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"text": "Most prior work (e.g., Blacoe and Lapata (2012; Hu et al. (2014) ) has neglected the need for multigranular comparability and performed matching within fixed levels only, e.g., only words were matched with words or only sentences with sentences. For a general solution to the problem of matching, we instead need the ability to match a unit on a lower level of granularity in one chunk with a unit on a higher level of granularity in the other chunk. Unlike (Socher et al., 2011) , our model does not rely on parsing and it can more exhaustively search the hypothesis space of possible matchings, including matchings that correspond to conflicting segmentations of the input chunks (see Section 5).",
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"text": "Our second contribution is that MultiGranCNN contains a flexible and modularized match feature component. This component computes the basic features that measure how well phrases of the two chunks match. We investigate three different match feature models that demonstrate that a wide variety of different match feature models can be implemented. The match feature models can be swapped in and out of MultiGranCNN, depending on the characteristics of the task to be solved.",
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"text": "Prior work that has addressed matching tasks has usually focused on a single task like QA (Bordes et al., 2014a; Yu et al., 2014) or paraphrasing (Socher et al., 2011; Madnani et al., 2012; Ji and Eisenstein, 2013) . The ARC architectures proposed by Hu et al. (2014) are intended to be more general, but seem to be somewhat limited in their flexibility to model different matching relations; e.g., they do not perform well for paraphrasing.",
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"text": "Different match feature models may also be required by factors other than the characteristics of the task. If the amount of labeled training data is small, then we may prefer a match feature model with few parameters that is robust against overfitting. If there is lots of training data, then a richer match feature model may be the right choice. This motivates the need for an architecture like MultiGranCNN that allows selection of the taskappropriate match feature model from a range of different models and its seamless integration into the architecture.",
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"text": "In remaining parts, Section 2 introduces some related work; Section 3 gives an overview of the proposed MultiGranCNN; Section 4 shows how to learn representations for generalized phrases (gphrases); Section 5 describes the three matching models: DIRECTSIM, INDIRECTSIM and CON-CAT; Section 6 describes the two 2D pooling methods: grid-based pooling and phrase-based pooling; Section 7 describes the match feature CNN; Section 8 summarizes the architecture of MultiGran CNN; and Section 9 presents experiments; finally, Section 10 concludes.",
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"text": "Paraphrase identification (PI) is a typical task of sentence matching and it has been frequently studied (Qiu et al., 2006; Blacoe and Lapata, 2012; Madnani et al., 2012; Ji and Eisenstein, 2013) . Socher et al. (2011) utilized parsing to model the hierarchical structure of sentences and uses unfolding recursive autoencoders to learn representations for single words and phrases acting as nonleaf nodes in the tree. The main difference to MultiGranCNN is that we stack multiple convolution layers to model flexible phrases and learn representations for them, and aim to address more general sentence correspondence. Bach et al. (2014) claimed that elementary discourse units obtained by segmenting sentences play an important role in paraphrasing. Their conclusion also endorses (Socher et al., 2011) 's and our work, for both take interactions between component phrases into account.",
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"text": "QA is another representative sentence matching problem. Yu et al. (2014) modeled sentence representations in a simplified CNN, finally finding the match score by projecting question and answer candidates into the same space. Other relevant QA work includes (Bordes et al., 2014c; Bordes et al., 2014a; Yang et al., 2014; Iyyer et al., 2014) For more general matching, Chopra et al. (2005) and Liu (2013) used a Siamese architecture of shared-weight neural networks (NNs) to model two objects simultaneously, matching their representations and then learning a specific type of sentence relation. We adopt parts of their architecture, but we model phrase representations as well as sentence representations.",
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"section": "Related Work",
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"text": "Li and Xu (2012) gave a comprehensive introduction to query-document matching and argued that query and document match at different levels: term, phrase, word sense, topic, structure etc. This also applies to sentence matching. Lu and Li (2013) addressed matching of short texts. Interactions between the two texts were obtained via LDA (Blei et al., 2003) and were then the basis for computing a matching score. Compared to MultiGranCNN, drawbacks of this approach are that LDA parameters are not optimized for the specific task and that the interactions are formed on the level of single words only. Gao et al. (2014) modeled interestingness between two documents with deep NNs. They mapped source-target document pairs to feature vectors in a latent space in such a way that the distance between the source document and its corresponding interesting target in that space was minimized. Interestingness is more like topic relevance, based mainly on the aggregated meaning of keywords, as opposed to more structural relationships as is the case for paraphrasing and clause coherence.",
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"text": "LDA (Blei et al., 2003)",
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"text": "We briefly discussed (Hu et al., 2014) 's ARC in Section 1. MultiGranCNN is partially inspired by ARC, but introduces multigranular comparability (thus enabling crosslevel matching) and supports a wider range of match feature models.",
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"text": "Our unsupervised learning component (Section 4, last paragraph) resembles word2vec CBOW (Mikolov et al., 2013) , but learns representations of TEXTCHUNKS as well as words. It also resembles PV-DM (Le and Mikolov, 2014), but our TEXTCHUNK representation is derived using a hierarchical architecture based on convolution and pooling.",
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"text": "We use convolution-plus-pooling in two different components of MultiGranCNN. The first component, the generalized phrase CNN (gpCNN), will be introduced in Section 4. This component learns representations for generalized phrases (gphrases) where a generalized phrase is a general term for subsequences of all granularities: words, short phrases, long phrases and the sentence itself. The gpCNN architecture has L layers of convolution, corresponding (for L = 2) to words, short phrases, long phrases and the sentence. We test different values of L in our experiments. We train gpCNN on large data in an unsupervised manner and then fine-tune it on labeled training data.",
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"text": "Using a Siamese configuration, two copies of gpCNN, one for each of the two input TEXTCHUNKS, are the input to the match feature model, presented in Section 5. This model produces s 1 \u00d7 s 2 matching features, one for each pair of g-phrases in the two chunks, where s 1 , s 2 are the number of g-phrases in the two chunks, respectively.",
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"text": "The s 1 \u00d7s 2 match feature matrix is first reduced to a fixed size by dynamic 2D pooling. The re-sulting fixed size matrix is then the input to the second convolution-plus-pooling component, the match feature CNN (mfCNN) whose output is fed to a multilayer perceptron (MLP) that produces the final match score. Section 6 will give details.",
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"text": "We use convolution-plus-pooling for both word sequences and match features because we want to compute increasingly abstract features at multiple levels of granularity. To ensure that g-phrases are mutually comparable when computing the s 1 \u00d7 s 2 match feature matrix, we impose the constraint that all g-phrase representations live in the same space and have the same dimensionality. ",
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"text": "We use several stacked blocks, i.e., convolutionplus-pooling layers, to extract increasingly abstract features of the TEXTCHUNK. The input to the first block are the words of the TEXTCHUNK, represented by CW (Collobert and Weston, 2008) embeddings. Given a TEXTCHUNK of length |S|, let vector c i \u2208 R wd be the concatenated embeddings of words v i\u2212w+1 , . . . , v i where w = 5 is the filter width, d = 50 is the dimensionality of CW embeddings and 0 < i < |S| + w. Embeddings for words v i , i < 1 and i > |S|, are set to zero. We then generate the representation",
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"start": 208,
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"text": "(Collobert and Weston, 2008)",
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"text": "p i \u2208 R d of the g-phrase v i\u2212w+1 , .",
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"text": ". . , v i using the convolution matrix W l \u2208 R d\u00d7wd :",
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"text": "EQUATION",
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"start": 0,
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"raw_str": "p i = tanh(W l c i + b l )",
"eq_num": "(1)"
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"sec_num": "4"
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"text": "where block index l = 1, bias b l \u2208 R d . We use wide convolution (i.e., we apply the convolution matrix W l to words v i , i < 1 and i > |S|) because this makes sure that each word v i , 1 \u2264 i \u2264 |S|, can be detected by all weights of W l -as opposed to only the rightmost (resp. leftmost) weights for initial (resp. final) words in narrow convolution. The configuration of convolution layers in following blocks (l > 1) is exactly the same except that the input vectors c i are not words, but the output of pooling from the previous layer of convolution -as we will explain presently. The configuration is the same (e.g., all W l \u2208 R d\u00d7wd ) because, by design, all g-phrase representations have the same dimensionality d. This also ensures that each g-phrase representation can be directly compared with each other g-phrase representation.",
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"text": "We use dynamic k-max pooling to extract the k l top values from each dimension after convolution in the l th block and the k L top values in the final block. We set",
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"start": 0,
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"text": "EQUATION",
"ref_id": "EQREF",
"raw_str": "k l = max(\u03b1, L \u2212 l L |S| )",
"eq_num": "(2)"
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"text": "where",
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"section": "gpCNN: Learning Representations for g-Phrases",
"sec_num": "4"
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"text": "l = 1, \u2022 \u2022 \u2022 , L",
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"section": "gpCNN: Learning Representations for g-Phrases",
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"text": "is the block index, and \u03b1 = 4 is a constant (cf. Kalchbrenner et al. (2014) ) that makes sure a reasonable minimum number of values is passed on to the next layer. We set k L = 1 (not 4, cf. Kalchbrenner et al. (2014) ) because our design dictates that all g-phrase representations, including the representation of the TEXTCHUNK itself, have the same dimensionality. Example: for L = 4, |S| = 20, the k i are [15, 10, 5, 1]. Dynamic k-max pooling keeps the most important features and allows us to stack multiple blocks to extract hiearchical features: units on consecutive layers correspond to larger and larger parts of the TEXTCHUNK thanks to the subset selection property of pooling. For many tasks, labeled data for training gpCNN is limited. We therefore employ unsupervised training to initialize gpCNN as shown in Figure 2 . Similar to CBOW (Mikolov et al., 2013) , we predict a sampled middle word v i from the average of seven vectors: the TEXTCHUNK representation (the final output of gpCNN) and the three words to the left and to the right of v i . We use noise-contrastive estimation (Mnih and Teh, 2012) for training: 10 noise words are sampled for each true example. Figure 3 : General illustration of match feature model. In this example, both S 1 and S 2 have 10 gphrases, so the match feature matrixF \u2208 R s 1 \u00d7s 2 has size 10 \u00d7 10.",
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"start": 49,
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"text": "Kalchbrenner et al. (2014)",
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"start": 1182,
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"text": "Figure 3",
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"text": "Let g 1 , . . . , g s k be an enumeration of the s k gphrases of TEXTCHUNK S k . Let S k \u2208 R s k \u00d7d be the matrix, constructed by concatenating the four matrices of unigram, short phrase, long phrase and sentence representations shown in Figure 2 that contain the learned representations from Section 4 for these s k g-phrases; i.e., row S ki is the learned representation of g i .",
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{
"start": 238,
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"text": "Figure 2",
"ref_id": "FIGREF1"
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"section": "Match Feature Models",
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"text": "The basic design of a match feature model is that we produce an s 1 \u00d7 s 2 matrixF for a pair of TEXTCHUNKS S 1 and S 2 , shown in Figure 3 . F i,j is a score that assesses the relationship between g-phrase g i of S 1 and g-phrase g j of S 2 with respect to the TEXTCHUNK relation of interest (paraphrasing, clause coherence etc). This scoreF i,j is computed based on the vector representations S 1i and S 2j of the two g-phrases. 1 We experiment with three different feature models to compute the match scoreF i,j because we would like our architecture to address a wide variety of different TEXTCHUNK relations. We can model a TEXTCHUNK relation like paraphrasing as \"for each meaning element in one sentence, there must be a similar meaning element in the other sentence\"; thus, a good candidate for the match scoreF i,j is simply vector similarity. In contrast, similarity is a less promising match score for clause coherence; for clause coherence, we want a score that models how good a continuation one g-phrase is for the other. These considerations motivate us to define three different match feature models that we will introduce now.",
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"start": 130,
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"text": "The first match feature model is DIRECTSIM. This model computes the match score of two gphrases as their similarity using a radial basis function kernel:",
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{
"text": "EQUATION",
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{
"start": 0,
"end": 8,
"text": "EQUATION",
"ref_id": "EQREF",
"raw_str": "F i,j = exp( \u2212||S 1i \u2212 S 2j || 2 2\u03b2 )",
"eq_num": "(3)"
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"sec_num": "5"
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"text": "where we set \u03b2 = 2 (cf. Wu et al. (2013) ). DIRECTSIM is an appropriate feature model for TEXTCHUNK relations like paraphrasing because in that case direct similarity features are helpful in assessing meaning equivalence.",
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{
"start": 24,
"end": 40,
"text": "Wu et al. (2013)",
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"text": "The second match feature model is INDIRECT-SIM. Instead of computing the similarity directly as we do for DIRECTSIM, we first transform the representation of the g-phrase in one TEXTCHUNK using a transformation matrix M \u2208 R d\u00d7d , then compute the match score by inner product and sigmoid activation:",
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{
"text": "EQUATION",
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{
"start": 0,
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"text": "EQUATION",
"ref_id": "EQREF",
"raw_str": "F i,j = \u03c3(S 1i MS T 2j + b),",
"eq_num": "(4)"
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"sec_num": "5"
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"text": "Our motivation is that for a TEXTCHUNK relation like clause coherence, the two TEXTCHUNKS need not have any direct similarity. However, if we map the representations of TEXTCHUNK S 1 into an appropriate space then we can hope that similarity between these transformed representations of S 1 and the representations of TEXTCHUNK S 2 do yield useful features. We will see that this hope is borne out by our experiments.",
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"text": "The third match feature model is CONCAT. This is a general model that can learn any weighted combination of the values of the two vectors:",
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"sec_num": "5"
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"text": "EQUATION",
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{
"start": 0,
"end": 8,
"text": "EQUATION",
"ref_id": "EQREF",
"raw_str": "F i,j = \u03c3(w T e i,j + b)",
"eq_num": "(5)"
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"sec_num": "5"
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"text": "where e i,j \u2208 R 2d is the concatenation of S 1i and S 2j . We can learn different combination weights w to solve different types of TEXTCHUNK matching.",
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"text": "We call this match feature model CONCAT because we implement it by concatenating g-phrase vectors to form a tensor as shown in Figure 4 .",
"cite_spans": [],
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{
"start": 127,
"end": 135,
"text": "Figure 4",
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"text": "The match feature models implement multigranular comparability: they match all units in one TEXTCHUNK with all units in the other TEXTCHUNK. This is necessary because a general solution to matching must match a low-level unit like \"reignite\" to a higher-level unit like \"fan the flames of\" (Figure 1 ). Unlike (Socher et al., 2011) , our model does not rely on parsing; therefore, it can more exhaustively search the hypothesis space of possible matchings: mfCNN covers a wide variety of different, possibly overlapping units, not just those of a single parse tree.",
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"start": 310,
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"start": 290,
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"text": "The match feature models generate an s 1 \u00d7 s 2 matrix. Since it has variable size, we apply two different dynamic 2D pooling methods, grid-based pooling and phrase-focused pooling, to transform it to a fixed size matrix.",
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"text": "We need to mapF \u2208 R s 1 \u00d7s 2 into a matrix F of fixed size s * \u00d7 s * where s * is a parameter. Gridbased pooling dividesF into s * \u00d7 s * nonoverlapping (dynamic) pools and copies the maximum value in each dynamic pool to F. This method is similar to (Socher et al., 2011) , but preserves locality better.",
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"text": "F can be split into equal regions only if both s 1 and s 2 are divisible by s * . Otherwise, for s 1 > s * and if s 1 mod s * = b, the dynamic pools in the first s * \u2212 b splits each have s 1 s * rows while the remaining b splits each have s 1 s * + 1 rows. In Figure 5 , a s 1 \u00d7 s 2 = 4 \u00d7 5 matrix (left) is split into s * \u00d7 s * = 3 \u00d7 3 dynamic pools (middle): each row is split into [1, 1, 2] and each column is split into [1, 2, 2].",
"cite_spans": [],
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{
"start": 260,
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"text": "Figure 5",
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"text": "If s 1 < s * , we first repeat all rows in batch style with size s 1 until no fewer than s * rows remain. Then the first s * rows are kept and split into s * dynamic pools. The same principle applies to the partitioning of columns. In Figure 5 (right) , the areas with dashed lines and dotted lines are repeated parts for rows and columns, respectively; each cell is its own dynamic pool.",
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{
"start": 235,
"end": 251,
"text": "Figure 5 (right)",
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"text": "In the match feature matrixF \u2208 R s 1 \u00d7s 2 , row i (resp. column j) contains all feature values for gphrase g i of S 1 (resp. g j of S 2 ). Phrase-focused pooling attempts to pick the largest match features Figure 5 : Partition methods in grid-based pooling. Original matrix with size 4 \u00d7 5 is mapped into matrix with size 3 \u00d7 3 and matrix with size 6 \u00d7 7, respectively. Each dynamic pool is distinguished by a border of empty white space around it.",
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"start": 206,
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"text": "Figure 5",
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"sec_num": "6.2"
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"text": "for a g-phrase g on the assumption that they are the best basis for assessing the relation of g with other g-phrases. To implement this, we sort the values of each row i (resp. each column j) in decreasing order giving us a matrixF r \u2208 R s 1 \u00d7s 2 with sorted rows (resp.F c \u2208 R s 1 \u00d7s 2 with sorted columns). Then we concatenate the columns ofF r (resp. the rows ofF c ) resulting in list",
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"text": "F r = {f r 1 , . . . , f r s 1 s 2 } (resp. F c = {f c 1 , . . . , f c s 1 s 2 }) where each f r (f c )",
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"text": "is an element ofF r (F c ). These two lists are merged into a list F by interleaving them so that members from F r and F c alternate. F is then used to fill the rows of F from top to bottom with each row being filled from left to right. 2",
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"text": "The output of dynamic 2D pooling is further processed by the match feature CNN (mfCNN) as depicted in Figure 6 . mfCNN extracts increasingly abstract interaction features from lower-level interaction features, using several layers of 2D wide convolution and fixed-size 2D pooling.",
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"start": 102,
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"text": "Figure 6",
"ref_id": "FIGREF3"
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"text": "We call the combination of a 2D wide convolution layer and a fixed-size 2D pooling layer a block, denoted by index b (b = 1, 2 . . .) . In general, let tensor T b \u2208 R c b \u00d7s b \u00d7s b denote the feature maps in block b; block b has c b feature maps, each of size s b \u00d7 s b (T 1 = F \u2208 R 1\u00d7s * \u00d7s * ). Let W b \u2208 R c b+1 \u00d7c b \u00d7f b \u00d7f b be the filter weights of 2D wide convolution in block b, f b \u00d7f b is then the size of sliding convolution regions. Then the convolution is performed as element-wise multiplication 2 IfF has fewer cells than F, then we simply repeat the filling procedure to fill all cells.",
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"text": "between W b and T b as follows:",
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"text": "T b+1 m,i\u22121,j\u22121 = \u03c3( W b m,:,:,: T b :,i\u2212f b :i,j\u2212f b :j +b b m ) (6) where 0\u2264m<c b+1 , 1 \u2264 i, j < s b +f b , b b \u2208 R c b+1 .",
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"text": "Subsequently, fixed-size 2D pooling selects dominant features from k b \u00d7 k b non-overlapping windows ofT b+1 to form a tensor as input of block b + 1:",
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"text": "T b+1 m,i,j = max(T b+1 m,ik b :(i+1)k b ,jk b :(j+1)k b ) (7)",
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"text": "where Hu et al. (2014) used narrow convolution which would limit the number of blocks. 2D wide convolution in this work enables to stack multiple blocks of convolution and pooling to extract higher-level interaction features. We will study the influence of the number of blocks on performance below.",
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"text": "For the experiments, we set",
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"text": "s * = 40, c b = 50, f b = 5, k b = 2 (b = 1, 2, \u2022 \u2022 \u2022).",
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"text": "We can now describe the overall architecture of MultiGranCNN. First, using a Siamese configuration, two copies of gpCNN, one for each of the two input TEXTCHUNKS, produce g-phrase representations on different levels of abstraction (Figure 2 ). Then one of the three match feature models (DIRECTSIM, CONCAT or INDIRECTSIM) produces an s 1 \u00d7 s 2 match feature matrix, each cell of which assesses the match of a pair of gphrases from the two chunks. This match feature matrix is reduced to a fixed size matrix by dynamic 2D pooling (Section 6). As shown in Figure 6 , the resulting fixed size matrix is the input for mfCNN, which extracts interaction features of increasing complexity from the basic interaction features computed by the match feature model. Finally, the output of the last block of mfCNN is the input to an MLP that computes the match score.",
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"text": "MultiGranCNN bears resemblance to previous work on clause and sentence matching (e.g., Hu et al. (2014 ), Socher et al. (2011 ), but it is more general and more flexible. It learns representations of g-phrases, i.e., representations of parts of the TEXTCHUNK at multiple granularities, not just for a single level such as the sentence as ARC-I does (Hu et al., 2014) . MultiGranCNN explores the space of interactions between the two chunks more exhaustively by considering interactions between every unit in one chunk with every other unit in the other chunk, at all levels of granularity. Finally, MultiGranCNN supports a number of different match feature models; the corresponding module can be instantiated in a way that ensures that match features are best suited to support accurate decisions on the TEXTCHUNK relation task that needs to be addressed.",
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"text": "Suppose the triple (x, y + , y \u2212 ) is given and x matches y + better than y \u2212 . Then our objective is the minimization of the following ranking loss:",
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"text": "where s(x, y) is the predicted match score for (x, y). We use stochastic gradient descent with Adagrad (Duchi et al., 2011) , L 2 regularization and minibatch training.",
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"text": "Recall that we employ unsupervised pretraining of representations for g-phrases. We can either freeze these representations in subsequent supervised training; or we can fine-tune them. We study the performance of both regimes.",
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"text": "As introduced by Hu et al. (2014), the clause coherence task determines for a pair (x, y) of clauses if the sentence \"xy\" is a coherent sentence. We construct a clause coherence dataset as follows (the set used by Hu et al. (2014) is not yet available). We consider all sentences from English Gigaword (Parker et al., 2009) that consist of two comma-separated clauses x and y, with each clause having between five and 30 words. For each y, we choose four clauses y . . . y randomly from the 1000 second clauses that have the highest similarity to y, where similarity is cosine similarity of TF-IDF vectors of the clauses; restricting the alternatives to similar clauses ensures that the task is hard. The clause coherence task then is to select y from the set y, y , . . . , y as the correct continuation of x. We create 21 million examples, each consisting of a first clause x and five second clauses. This set is divided into a training set of 19 million and development and test sets of one million each. An example from the training set is given in Figure 1 .",
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"text": "Then, we study the performance variance of different MultiGranCNN setups from three perspectives: a) layers of CNN in both unsupervised (gpCNN) and supervised (mfCNN) training phases; b) different approaches for clause relation feature modeling; c) dynamic pooling methods for generating same-sized feature matrices. Figure 7 (top table) shows that (Hu et al., 2014) 's parameters are good choices for our setup as well. We get best result when both gpCNN and mfCNN have three blocks of convolution and pooling. This suggests that multiple layers of convolution succeed in extracting high-level features that are beneficial for clause coherence. Figure 7 (2nd table) shows that INDIRECTSIM and CONCAT have comparable performance and both outperform DIRECTSIM. DIRECTSIM is expected to perform poorly because the contents in the two clauses usually have little or no overlapping meaning. In contrast, we can imagine that INDIRECTSIM first transforms the first clause x into a counterpart and then matches this counterpart with the second clause y. In CONCAT, each of s 1 \u00d7s 2 pairs of g-phrases is concatentated and supervised training can then learn an unrestricted function to assess the importance of this pair for clause coherence (cf. Eq. 5). Again, this is clearly a more promising TEXTCHUNK relation model for clause coherence than one that relies on DIRECT-SIM. (3rd table) demonstrates that finetuning g-phrase representations gives better performance than freezing them. Also, grid-based and phrase-focused pooling outperform dynamic pooling (Socher et al., 2011) (4th table) . Phrasefocused pooling performs best. Table 1 compares MultiGranCNN to ARC-I and ARC-II, the architectures proposed by Hu et al. (2014) . We also test the five baseline systems from their paper: DeepMatch, WordEmbed, SEN-MLP, SENNA+MLP, URAE+MLP. For Multi-GranCNN, we use the best dev set settings: number of convolution layers in gpCNN and mfCNN is 3; INDIRECTSIM; phrase-focused pooling. Table 1 shows that MultiGranCNN outperforms all other approaches on clause coherence test set.",
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"text": "We evaluate paraphrase identification (PI) on the PAN corpus (http://bit.ly/mt-para, (Madnani et al., 2012) ), consisting of training and test sets of 10,000 and 3000 sentence pairs, respectively. Sentences are about 40 words long on average.",
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"text": "Since PI is a binary classification task, we replace the MLP with a logistic regression layer. As phrase-focused pooling was proven to be optimal, we directly use phrase-focused pooling in PI task without comparison, assuming that the choice of dynamic pooling is task independent.",
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"text": "For parameter selection, we split the PAN training set into a core training set (core) of size 9000 and a development set (dev) of size 1000. We then train models on core and select parameters based on best performance on dev. The best results on dev are obtained for the following parameters: freezing g-phrase representations, DIRECT-SIM, two convolution layers in gpCNN, no convolution layers in mfCNN. We use these parameter settings to train a model on the entire training set and report performance in Table 2 .",
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"text": "We compare MultiGranCNN to ARC-I/II (Hu et al., 2014) , and two previous papers reporting performance on PAN. Madnani et al. (2012) 94.9 94.7 Table 2 : Results on PAN. \"8MT\" = 8 MT metrics Table 2 shows that MultiGranCNN in combination with MT metrics obtains state-of-the-art performance on PAN. Freezing weights learned in unsupervised training ( Figure 2 ) performs better than fine-tuning them; also, Table 3 shows that the best result is achieved if no convolution is used in mfCNN. Thus, the best configuration for paraphrase identification is to \"forward\" fixed-size interaction matrices as input to the logistic regression, without any intermediate convolution layers.",
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"text": "Freezing weights learned in unsupervised training and no convolution layers in mfCNN both protect against overfitting. Complex deep neural networks are in particular danger of overfitting when training sets are small as in the case of PAN (cf. Hu et al. (2014) ). In contrast, fine-tuning weights and several convolution layers were the optimal setup for clause coherence. For clause coherence, we have a much larger training set and therefore can successfully train a much larger number of parameters. Table 3 shows that CONCAT performs badly for PI while DIRECTSIM and INDIRECTSIM perform well. We can conceptualize PI as the task of determining if each meaning element in S 1 has a similar meaning element in S 2 . The s 1 \u00d7 s 2 DIRECT-SIM feature model directly models this task and the s 1 \u00d7s 2 INDIRECTSIM feature model also models it, but learning a transformation of g-phrase representations before applying similarity. In contrast, CONCAT can learn arbitrary relations between parts of the two sentences, a model that seems to be too unconstrained for PI if insufficient training resources are available.",
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"text": "In contrast, for the clause coherence task, concatentation worked well and DIRECTSIM worked poorly and we provided an explanation based on the specific properties of clause coherence (see discussion of Figure 7) . We conclude from these results that it is dependent on the task what the best feature model is for matching two linguistic objects. Interestingly, INDIRECTSIM performs well on both tasks. This suggests that INDIRECTSIM is a general feature model for matching, applicable to tasks with very different properties.",
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"text": "In this paper, we present MultiGranCNN, a general deep learning architecture for classifying the relation between two TEXTCHUNKS. Multi-GranCNN supports multigranular comparability of representations: shorter sequences in one TEXTCHUNK can be directly compared to longer sequences in the other TEXTCHUNK. Multi-GranCNN also contains a flexible and modularized match feature component that is easily adaptable to different TEXTCHUNK relations. We demonstrated state-of-the-art performance of MultiGranCNN on paraphrase identification and clause coherence tasks.",
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"sec_num": "10"
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"text": "In response to a reviewer question, recall that si is the total number of g-phrases of Si, so there is only one s1 \u00d7 s2 matrix, not several on different levels of granularity.",
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"text": "Thanks to CIS members and anonymous reviewers for constructive comments. This work was supported by Baidu (through a Baidu scholarship awarded to Wenpeng Yin) and by Deutsche Forschungsgemeinschaft (grant DFG SCHU 2246/8-2, SPP 1335).",
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"ref_entries": {
"FIGREF0": {
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"text": "Examples for paraphrasing and clause coherence tasks"
},
"FIGREF1": {
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"type_str": "figure",
"text": "gpCNN: learning g-phrase representations. This figure only shows two convolution layers (i.e., L = 2) for saving space."
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"text": "CONCAT match feature model"
},
"FIGREF3": {
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"num": null,
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"text": "mfCNN & MLP for matching score learning. s * = 10, f b = 5, k b = 2, c b = 4 in this example."
},
"FIGREF4": {
"uris": null,
"num": null,
"type_str": "figure",
"text": "Effect on dev acc (clause coherence) of different factors: # convolution blocks, match feature model, freeze vs. fine-tune, pooling method."
},
"FIGREF5": {
"uris": null,
"num": null,
"type_str": "figure",
"text": "Figure 7 (3rd table) demonstrates that finetuning g-phrase representations gives better performance than freezing them. Also, grid-based and phrase-focused pooling outperform dynamic pooling (Socher et al., 2011) (4th table). Phrasefocused pooling performs best. Table 1 compares MultiGranCNN to ARC-I and ARC-II, the architectures proposed by Hu et al."
},
"TABREF1": {
"num": null,
"type_str": "table",
"text": "Performance on clause coherence test set. SEPIA), computed on entire sentences. Bach et al. (2014) applied MT metrics to elementary discourse units. We integrate these eight MT metrics from prior work.",
"content": "<table><tr><td>used</td></tr></table>",
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