acl acl2010 acl2010-246 knowledge-graph by maker-knowledge-mining
Source: pdf
Author: Minwoo Jeong ; Ivan Titov
Abstract: Documents often have inherently parallel structure: they may consist of a text and commentaries, or an abstract and a body, or parts presenting alternative views on the same problem. Revealing relations between the parts by jointly segmenting and predicting links between the segments, would help to visualize such documents and construct friendlier user interfaces. To address this problem, we propose an unsupervised Bayesian model for joint discourse segmentation and alignment. We apply our method to the “English as a second language” podcast dataset where each episode is composed of two parallel parts: a story and an explanatory lecture. The predicted topical links uncover hidden re- lations between the stories and the lectures. In this domain, our method achieves competitive results, rivaling those of a previously proposed supervised technique.
Reference: text
sentIndex sentText sentNum sentScore
1 de Abstract Documents often have inherently parallel structure: they may consist of a text and commentaries, or an abstract and a body, or parts presenting alternative views on the same problem. [sent-4, score-0.283]
2 Revealing relations between the parts by jointly segmenting and predicting links between the segments, would help to visualize such documents and construct friendlier user interfaces. [sent-5, score-0.333]
3 To address this problem, we propose an unsupervised Bayesian model for joint discourse segmentation and alignment. [sent-6, score-0.478]
4 We apply our method to the “English as a second language” podcast dataset where each episode is composed of two parallel parts: a story and an explanatory lecture. [sent-7, score-0.717]
5 The predicted topical links uncover hidden re- lations between the stories and the lectures. [sent-8, score-0.184]
6 1 Introduction Many documents consist of parts exhibiting a high degree of parallelism: e. [sent-10, score-0.17]
7 , abstract and body of academic publications, summaries and detailed news stories, etc. [sent-12, score-0.044]
8 0 technologies: many texts on the web are now accompanied with comments and discussions. [sent-14, score-0.032]
9 Segmentation of these parallel parts into coherent fragments and discovery of hidden relations between them would facilitate the development of better user interfaces and improve the performance of summarization and information retrieval systems. [sent-15, score-0.447]
10 Discourse segmentation of the documents composed of parallel parts is a novel and challenging problem, as previous research has mostly focused on the linear segmentation of isolated texts (e. [sent-16, score-0.891]
11 The most straightforward approach would be to use a pipeline strategy, where an existing segmentation algorithm finds discourse boundaries of each part independently, and then the segments are aligned. [sent-19, score-0.783]
12 Or, conversely, a sentence-alignment stage can be followed by a segmentation stage. [sent-20, score-0.261]
13 However, as we will see in our experiments, these strategies may result in poor segmentation and alignment quality. [sent-21, score-0.359]
14 To address this problem, we construct a nonparametric Bayesian model for joint segmentation and alignment of parallel parts. [sent-22, score-0.544]
15 In comparison with the discussed pipeline approaches, our method has two important advantages: (1) it leverages the lexical cohesion phenomenon (Halliday and Hasan, 1976) in modeling the parallel parts of documents, and (2) ensures that the effective number of segments can grow adaptively. [sent-23, score-0.767]
16 Lexical cohesion is an idea that topicallycoherent segments display compact lexical distributions (Hearst, 1994; Utiyama and Isahara, 2001 ; Eisenstein and Barzilay, 2008). [sent-24, score-0.412]
17 We hypothesize that not only isolated fragments but also each group of linked fragments displays a compact and consistent lexical distribution, and our generative model leverages this inter-part cohesion assumption. [sent-25, score-0.507]
18 In this paper, we consider the dataset of “English as a second language” (ESL) podcast1 ,where each episode consists of two parallel parts: a story (an example monologue or dialogue) and an explanatory lecture discussing the meaning and usage of English expressions appearing in the story. [sent-26, score-0.65]
19 1 presents an example episode, consisting of two parallel parts, and their hidden topical relations. [sent-28, score-0.21]
20 2 From the figure we may conclude that there is a tendency of word repetition between each pair of aligned segments, illustrating our hypothesis of compactness of their joint distribution. [sent-29, score-0.094]
21 c C2o0n1f0er Aenscseoc Sihatoirotn P faopre Crso,m papguetsat 1io5n1a–l1 L5i5n,guistics St ory Lecture t ran s cript Thispodcast is all about business vocabulary related to accounting. [sent-37, score-0.036]
22 Is mha lve b au sdinaeys sjo bon, tbhuet sI idre c. [sent-38, score-0.032]
23 ently started a TATh de a ytsi t ljo erb yo fibs teh ygeoi npusro rdbecyga uMslta rigs jd oBabul etshnina et s yasoy uBin owgo okthrk ae ta estp hifnreo gmh. [sent-39, score-0.047]
24 this ats yo uy oneu edca tno RA c oc blmaonouaodn ktabiknegege gi pysniso ntubhg rye. [sent-50, score-0.047]
25 a pti nthg e croer aseocnt rtehcaotr dyso uo fn tehe d m to ndeoy y o u r sbpoeonkdk;e iet'psi nvge riys s iom yiloaur tcoa n . [sent-54, score-0.063]
26 means having enough money to run your business - to pay your bills. [sent-62, score-0.036]
27 to divide the lecture transcript into discourse units and to align each unit to the related segment of the story. [sent-65, score-0.631]
28 Predicting these structures for the ESL podcast could be the first step in development of an e-learning system and a podcast search engine for ESL learners. [sent-66, score-0.506]
29 2 Related Work Discourse segmentation has been an active area of research (Hearst, 1994; Utiyama and Isahara, 2001 ; Galley et al. [sent-67, score-0.261]
30 Our work extends the Bayesian segmentation model (Eisenstein and Barzilay, 2008) for isolated texts, to the problem of segmenting parallel parts of documents. [sent-69, score-0.606]
31 The task of aligning each sentence of an abstract to one or more sentences of the body has been studied in the context of summarization (Marcu, 1999; Jing, 2002; Daum e´ and Marcu, 2004). [sent-70, score-0.121]
32 Our work is different in that we do not try to extract the most relevant sentence but rather aim to find coherent fragments with maximally overlapping lexical distributions. [sent-71, score-0.158]
33 , (Daum ´e and Marcu, 2006)) is also related but it focuses on sentence extraction rather than on joint segmentation. [sent-74, score-0.043]
34 We are aware ofonly one previous work onjoint segmentation and alignment of multiple texts (Sun et al. [sent-75, score-0.423]
35 , 2007) but their approach is based on similarity functions rather than on modeling lexical cohesion in the generative framework. [sent-76, score-0.144]
36 Our application, the analysis of the ESL podcast, was previously studied in (Noh et al. [sent-77, score-0.033]
37 They proposed a supervised method which is driven by pairwise classification decisions. [sent-79, score-0.044]
38 The main drawback of their approach is that it neglects the discourse structure and the lexical cohesion phenomenon. [sent-80, score-0.281]
39 3 Model In this section we describe our model for discourse segmentation of documents with inherently parallel structure. [sent-81, score-0.706]
40 We start by clarifying our assumptions about their structure. [sent-82, score-0.054]
41 We assume that a document x consists of K {x(k) parallel parts, that is, x = }k=1:K, and peaarchal part aorft ,the th adotc ius,m exnt =con {sixsts o}f segments, = Note that the effective number of= fragments I unknown. [sent-83, score-0.266]
42 Each segment can is either be specific to this part (drawn from a part- x(k) {s(ik)}i=1:I. [sent-84, score-0.2]
43 φi(k)) specific language model or correspond to the entire document (drawn from a document-level language model For example, the first and the second sentences of the lecture transcript in Fig. [sent-85, score-0.418]
44 The document-level language models define topical links between segments in different parts of the document, whereas the part-specific language models define the linear segmentation of the remaining unaligned text. [sent-87, score-0.717]
45 Each document-level language model corresponds to the set of aligned segments, at most one segment per part. [sent-88, score-0.288]
46 Similarly, each part-specific language model corresponds to a single segment of the single corresponding part. [sent-89, score-0.237]
47 Note that all the documents are modeled independently, as we aim not to discover collection-level topics (as e. [sent-90, score-0.079]
48 , 2003)), but to perform joint discourse segmentation and alignment. [sent-94, score-0.441]
49 Unlike (Eisenstein and Barzilay, 2008), we cannot make an assumption that the number of segments is known a-priori, as the effective number of part-specific segments can vary significantly from document to document, depending on their size and structure. [sent-95, score-0.586]
50 To tackle this problem, we use Dirichlet processes (DP) (Ferguson, 1973) to de152 fine priors on the number of segments. [sent-96, score-0.066]
51 We incorporate them in our model in a similar way as it is done for the Latent Dirichlet Allocation (LDA) by Yu et al. [sent-97, score-0.037]
52 Unlike the standard LDA, the topic proportions are chosen not from a Dirichlet prior but from the marginal distribution GEM(α) defined by the stick breaking construction (Sethuraman, 1994), where α is the concentration parameter of the underlying DP distribution. [sent-99, score-0.149]
53 The formal definition ofour model is as follows: • • Draw the document-level topic proportions β(doc) GDErawM t(hαe(d doocc)u)m. [sent-101, score-0.186]
54 Choose the document-level language model φ(idoc) ∼ ∼ Dir(γ(doc)) for i ∈ {1, 2, . [sent-102, score-0.037]
55 • • • Draw the part-specific topic proportions β(k) ∼ GDrEawM( thαe(k) p)a frot-rs pke ∈ {1, . [sent-106, score-0.149]
56 If = Doc); draw topic ∼ β(doc); gen– – – t(nk) (t(nk) zn(k) erate words x(nk) ∼ Mult(φz((dnokc))) Otherwise; draw topic zn(k) β(k); words x(nk) ∼ Mult(φz((knk))). [sent-118, score-0.308]
57 ∼ γ(doc), γ(k), α(doc) generate α(k) The priors and can be estimated at learning time using non-informative hyperpriors (as we do in our experiments), or set manually to indicate preferences of segmentation granularity. [sent-119, score-0.399]
58 At inference time, we enforce each latent topic zn(k) to be assigned to a contiguous span of text, assuming that coherent topics are not recurring across the document (Halliday and Hasan, 1976). [sent-120, score-0.224]
59 In fact, this constraint can be integrated in the model definition but it would significantly complicate the model description. [sent-122, score-0.074]
60 At each iteration of the MH algorithm, a new potential alignment-segmentation pair (z0, t0) is drawn from a proposal distribution Q(z0, t0 |z, t), where (z, t) (a) (b) (c) Figure 2: Three types of moves: (a) shift, (b) split and (c) merge. [sent-124, score-0.098]
61 In order to implement the MH algorithm for our model, we need to define the set of potential moves (i. [sent-129, score-0.191]
62 admissible changes from (z, t) to (z0, t0)), and the proposal distribution Q over these moves. [sent-131, score-0.053]
63 If the actual number of segments is known and only a linear discourse structure is acceptable, then a single move, shift of the segment border (Fig. [sent-132, score-0.65]
64 In our case, however, a more complex set of moves is required. [sent-134, score-0.191]
65 We make two assumptions which are motivated by the problem considered in Section 5: we assume that (1) we are given the number of document-level segments and also that (2) the aligned segments appear in the same order in each part of the document. [sent-135, score-0.641]
66 With these assumptions in mind, we introduce two additional moves (Fig. [sent-136, score-0.245]
67 2(b) and (c)): • Split move: select a segment, and split it at one to fm tohvee spanned sentences; i afn nthde s segment was a document-level segment then one of the fragments becomes the same documentlevel segment. [sent-137, score-0.575]
68 • Merge move: select a pair of adjacent segMmeerngtse w mhoevree: :a ts eleleacstt one iorf o tfhe a segments gispart-specific, and merge them; if one of them was a document-level segment then the new segment has the same document-level topic. [sent-138, score-0.701]
69 All the moves are selected with the uniform probability, and the distance c for the shift move is drawn from the proposal distribution proportional to c−1/cmax. [sent-139, score-0.382]
70 Although the above two assumptions are not crucial as a simple modification to the set ofmoves would support both introduction and deletion of document-level fragments, this modification was not necessary for our experiments. [sent-141, score-0.054]
71 1 Dataset and setup Dataset We apply our model to the ESL podcast dataset (Noh et al. [sent-143, score-0.336]
72 , 2010) of 200 episodes, with an average of 17 sentences per story and 80 sentences per lecture transcript. [sent-144, score-0.322]
73 The gold standard alignments assign each fragment of the story to a segment of the lecture transcript. [sent-145, score-0.555]
74 We can induce segmentations at different levels of granularity on both the story and the lecture side. [sent-146, score-0.322]
75 However, given that the segmentation of the story was obtained by an automatic sentence splitter, there is no reason to attempt to reproduce this segmentation. [sent-147, score-0.397]
76 (2010) and restrict our model to alignment structures which agree with the given segmentation of the story. [sent-149, score-0.396]
77 Evaluation metrics To measure the quality of segmentation of the lecture transcript, we use two standard metrics, Pk (Beeferman et al. [sent-151, score-0.488]
78 , 1999) and WindowDiff (WD) (Pevzner and Hearst, 2002), but both metrics disregard the alignment links (i. [sent-152, score-0.18]
79 Consequently, we also use the macro-averaged F1 score on pairs of aligned span, which measures both the segmentation and alignment quality. [sent-155, score-0.41]
80 For the first baseline, we consider the pairwise sentence alignment (SentAlign) based on the unigram and bigram overlap. [sent-157, score-0.142]
81 The second baseline is a pipeline approach (Pipeline), where we first segment the lecture transcript with BayesSeg (Eisenstein and Barzilay, 2008) and then use the pairwise alignment to find their best alignment to the segments of the story. [sent-158, score-1.119]
82 Our model We evaluate our joint model of segmentation and alignment both with and without the split/merge moves. [sent-159, score-0.476]
83 For the model without these moves, we set the desired number of segments in the lecture to be equal to the actual number of segments in the story I. [sent-160, score-0.895]
84 In this setting, the moves can only adjust positions of the segment borders. [sent-161, score-0.433]
85 For the model with the split/merge moves, we start with the same number of segments I it can be increased or decreased during inbut ference. [sent-162, score-0.305]
86 362418345 2934 Table 1: Results on the ESL podcast dataset. [sent-166, score-0.253]
87 Also we perform L-BFGS optimization to automatically adjust the non-informative hyperpriors after each 1,000 iterations of sampling. [sent-170, score-0.114]
88 ‘Uniform’ denotes the minimal baseline which uniformly draws a random set of I spans for each lecture, and then aligns them to the segments of the story preserving the linear order. [sent-173, score-0.404]
89 Also, we consider two variants of the pipeline approach: segmenting the lecture on I 2I + 1segments, reand spectively. [sent-174, score-0.353]
90 The significant improvement over the pipeline results demonstrates benefits of joint modeling for the considered problem. [sent-178, score-0.16]
91 Moreover, additional benefits are obtained by using the DP priors and the split/merge moves (the last line in Table 1). [sent-179, score-0.257]
92 Finally, our model significantly outperforms the previously proposed supervised model (Noh et al. [sent-180, score-0.074]
93 This observation confirms that lexical cohesion modeling is crucial for suc- cessful discourse analysis. [sent-184, score-0.281]
94 6 Conclusions We studied the problem of joint discourse segmentation and alignment of documents with inherently parallel structure and achieved favorable results on the ESL podcast dataset outperforming the cascaded baselines. [sent-185, score-1.142]
95 Accurate prediction of these hidden relations would open interesting possibilities 3The use of the DP priors and the split/merge moves on the first stage of the pipeline did not result in any improvement in accuracy. [sent-186, score-0.423]
96 One example being an application which, given a userselected fragment of the abstract, produces a summary from the aligned segment of the document body. [sent-188, score-0.334]
97 The automatic construction of large-scale corpora for summarization research. [sent-243, score-0.044]
98 Script-description pair extraction from text documents of English as second language podcast. [sent-247, score-0.079]
99 Topic segmentation with shared topic detection and alignment of multiple documents. [sent-260, score-0.444]
wordName wordTfidf (topN-words)
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