DepNeCTI: Dependency-based Nested Compound Type Identification for Sanskrit
Authors: Jivnesh Sandhan
Yaswanth Narsupalli
Sreevatsa Muppirala
Sriram Krishnan
Pavankumar Satuluri
Amba Kulkarni
Pawan Goyal
Venue: EMNLP 2023 Findings (2023)
Keywords: Nested compound type identification for Sanskrit
word-level lexical semantics
newly annotated dataset
benchmarking and dependency-based novel framework.
TL;DR: In this work, we introduce a novel task, datasets and a framework for the nested compound type identification task in Sanskrit.

Abstract: Multi-component compounding is a prevalent phenomenon in Sanskrit, and understanding the implicit structure of a compound’s components is crucial for deciphering its meaning. Earlier approaches in Sanskrit have focused on binary compounds and neglected the multi-component compound setting. This work introduces the novel task of nested compound type identification (NeCTI), which aims to identify nested spans of a multi-component compound and decode the implicit semantic relations between them. To the best of our knowledge, this is the first attempt in the field of lexical semantics to propose this task. 

We present 2 newly annotated datasets including an out-of-domain dataset for this task. We also benchmark these datasets by exploring the efficacy of the standard problem formulations such as nested named entity recognition, constituency parsing and seq2seq, etc. We present a novel framework named DepNeCTI: Dependency-based Nested Compound Type Identifier that surpasses the performance of the best baseline with an average absolute improvement of 13.1 points F1-score in terms of Labeled Span Score (LSS) and a 5-fold enhancement in inference efficiency. In line with the previous findings in the binary Sanskrit compound identification task, context provides benefits for the NeCTI task. The codebase and datasets are publicly available at: https://github.com/yaswanth-iitkgp/DepNeCTI

DepNeCTI: Dependency-based Nested Compound Type Identification for Sanskrit
Jivnesh Sandhan'; Yaswanth Narsupalli"; Sreevatsa Sreevtts Muppirala', Sriram Krishnan Pavankumar Satuluri!, Amba Kulkarni and Pawan Goyal
UC Berkeley, FITT Kharagpur, UUniversity of Hyderabad and IIT Roorkee ji iineshaandaan@gaaill.. com, yasshu. yaw.huutaaaaatte@maiill 1 com pawangêcse ac. in
Abstract
2020). Within the literature, the NCI problem has been approached in two ways, namely, classifica- tion (Dima and Hinrichs, 2015; Fares et al., 2018; Ponkiya et al., 2021) and paraphrasing (Lapata and Keller, 2004; Ponkiya et al., 2018, 2020).
Multi-component compounding is prevalent phenomenon in Sanskrit, and understanding the implicit structure of a compound's components crucial for deciphering meaning. Earlier approaches in Sanskrit have focused binary compounds and neglected the multi-component compound setting. This work introduces the novel task of nested compound type identifi- cation (NeCTI), which aims to identify nested spans of multi-comppnent compound and de- code the implicit semantic relations between them. the best of our knowledge, this is first attempt in the field of lexical semantics to propose this task.
In Sanskrit literature, particularly in poetry, the use of multi-component compounds is ubiquitous (Kumar, 2012). According to the Digital Corpus of Sanskrit, more than 41 %% of compounds contain three or more components (Krishna et al., 2016). However, earlier approaches focus solely on binary compounds and fail to address the complexities in- herent in multi-component compounds adequately. Thus, propose new task, the Nested Com- pound Type Identification (NeCTI) Task, which focuses on identifying nested spans within a multi- component compound and interpreting their im- plicit semantic relationships. Figure 1 illustrates an example of the NeCTI task, highlighting nested spans and their associated semantic relations using distinct colors.
We present 2 newly annotated datasets includ- ing an out-of-domain dataset for this task. We also benchmark these datasets by exploring the efficacy of the standard problem formu- lations such as nested named entity recogni- tion, constituency parsing and seq2seq, etc. We present novel framework named DepNeCTI: Dependency-bere Nested Compound Type Identifier that surpasses the performance of the best baseline with an average absolute improve- of 13.1 points Fl-score in beled Span Score (LSS) and a 5-fold enhance- ment inference efficiency. In line with the previous findings in the binary Sanskrit pound identification task, context provides ben- efits for the NeCTI task. The codebase and datasets are publicly available at: https:// gi thub. com yaswanth-w-hhpp tkgp//eeNeeTT
sumiträ- aaiitra-annandaa-arhhaaaah
Endocentric (T6)
of La-
Endocentric (T6)
sumirra waaandd-aaddaaaah--6-66
T6
T6
Figure Illustration of the NeCTI task for the multi-component compound vardhanāh (Translation: seeiitt-ddlligh-eennnneeee highlighting nested spans and their associated semantic relations using distinct colors.
1 Introduction
A compound is defined as a group of entities func- tioning single meaningful entity. The process of identifying the implied semantic relationship be- tween the components of a compound in Sanskrit is known as Sanskrit Compound Type Identification (SaCTT) (Sandhan et al., 2022a) or Noun Com- pound Interpretation (NC1) (Ponkiya et al., 2021,
The NeCTI task presents multiple challenges: (1) The number of potential solutions for multi- component compound grows exponentially the number of components increases. (2) often relies on contextual or world knowledge about the enti- ties involved (Krishna et al., 2016). Even a multi- component compound shares the same components
denotes the first two authors contributed equally.

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and final form, the implicit relationship between the spans can only be deciphered with the aid of avail- able contextual information (Kulkarni and Kumar, 2013; Krishna et al., 2016). For instance, as de-
We present 2 newly annotated datasets and pro- vide benchmarking by exploring the efficacy of various standard formulations for NeCTI ($ 5).
We propose novel framework DepNeCTI:: Dependency-based Nested Compound type Iden- tifier ($ 4), which reports an average 13.1 points Fl-score in terms of LSS absolute gain and 5- fold enhancement in inference efficiency ($ 6) over the best baseline.
Endopentioo (T6)
Entaanced the delight of Sumitra
with context >
Endocentric
sumilrä- ananda vardhanah
lakemanalh
anujagäma
Copurativa
without context
>
Enrichment of Sumbrá and Ananda
Endocantrcc (T6)
Figure 2: Illustration of the multi-component pound sumitrā-anandee varnhaa- (Translation: Sum- iträ-delight- iträ-delighteenhaneer enhancer)) with two valid parses depicted in green and red. The two parses correspond to two potential meanings. The green solution represents the correct interpretation within the provided context.
We publicly release the codebase of DepNeCTI and benchmarked baselines, along with newly annotated datasets for the NcCTI task.
Problem Formulation
picted in Figure 2, the multi-component compound (Translation: Sumitrā- delight-enhancer) can have two valid but distinct solutions, leading to different meanings. Resolving ambiguity to select the correct solution requires reliance on the provided context. Consequently, downstream Natural Language Processing (NLP) applications for Sanskrit, such as question answer- ing (Terdalkar and Bhattacharya, 2019) and ma- chine translation (Aralikatte et al., 2021), often exhibit sub-optimal performance when encounter- ing compounds. Hence, the NeCTI task serves as a preliminary requirement for developing robust NLP technology for Sanskrit. Moreover, this de- pendency on contextual information eliminates the possibility of storing and conducting a lookup to identify the semantic types of nested spans.
The objective of the NeCTI task is to detect nested spans within multi-component compound and de- cipher the implicit semantic relations among them. Our study focuses exclusively on this task and does not address the compound segmentation problem. assumed that the segmented components of the multi-component compound are already available. To obtain the segmentation of compound, we rely on established resources such as the rule-based shallow parser (Goyal and Huet, 2016) exist- ing data-driven segmentation systems designed ex- plicitly for Sanskrit (Hellwig and Nehrdich, 2018; Sandhan et al., 2022b).
Number of ways of Parsing vs Number of components
Catalan Number
1em
Previous approaches (Kulkarni and Kumar, 2013; Krishna et al., 2016; Sandhan al., 2019) addressing SaCTI have predominantly focused on binary compounds, neglecting the consideration of multi-component compounds. In multi-component compounds, the components exhibit semantic re- lationships akin to dependency relations, repre- sented as directed labels within the dependency structure, which also facilitate the identification of the compound's headword through the labels directed towards it. Consequently, dependency for- mulation enables the simultaneous identification of both the structure or constituency span and the com- pound types. Thus, we propose a novel framework ($ 4) named DepNeCTI: Dependency-bartt based Nested Compound type Identifier ($3). In summary, our contributions can be outlined as follows:
2014
20t0
10.
Number of components
Figure 3: Illustration of number of ways (as per Cata- lan number log scale) in which a multi-component compound can be parsed. Our dataset contains com- pounds that have maximum of 16 components.
As the number of components in multi-componett compound increases. the number of possible parses grows exponentially. Our dataset encompasses multi- component compounds ranging from 2 16 com- ponents. Figure visually depicts the exponential growth of possible parses with increasing compo- nent count. The parsing of a compound word with
Complexity of NeCTI Task:
We introduce a novel task called Nested Com- pound Type Identification ($ 2).

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n + 1 components can be likened to the problem of fully parenthesizing n + factors in all possible ways (Kulkarni and Kumar, 20.1). Thus, the total number of parse-options for a multi-component compound word with n + 1 components corre- sponds to the Catalan Number Cn. where n 2 0 (Huet, 2009).
ities. Conversely, we could not find discussions related these aspects in NNER literature. (3) NeCTI benefits from prior knowledge of the com- pound's location and segmented components. contrast, the NNER task involves the additional challenge of identifying the location of entities within the text. Consequently, leveraging exist- ing NNER frameworks for NcCTI is not straight- forward due their inability to provide explicit support for providing the location of compounds. Therefore, NeCTI presents unique characteristics and challenges that differentiate it from the NNER task, requiring specialized approaches tailored to its specific requirements.
2m 12 (n 1)!m!
(2n)!
Cn
1++1
where C, represents the th Catalan number, (2n) is the binomial coefficient, and denotes fac- torial. Finally, the compatibility rules derived from Päninīan grammar (Panini, 500 BCE) and contex- tual information are needed disambiguate multi- ple possibilities.
How different is NeCTI compared to the Multi- Word Expressions (MWE)? MWES encompass various categories such as idioms (c.g., kick the bucket), named entities (e.g., World Health Orga- nization), and compounds (e.g., telephone box), etc. Sanskrit compounds exhibit similarities to multi-word expressions, particularly multi-word compounds (nominal, noun, and verb), with respect to characteristics like collocation of components based semantic relations between them and strict preference for ordering of the components.
Formally, in given sentence X (21, 22 ... UM) with M tokens, let Zp (1 $ P S M) denote an N-component compound. It is worth noting that X may contain multiple instances of multi-component compounds. A valid solution corresponds to full paranthesization of these N components; let PN encompass all valid solutions for fully parenthesizing N factors, satisfying the cardi- nality [Pvl CN where CN represents the Catalan number. A valid solution for an N N-component compound consists N nested spans. The NeCTI system produces an output represented as list of N - 1 tuples for Tp. given by Y, = {[T If. Tı), ................................ [T 1N 1, T.TN=1)) such that Y,, PN- I and 1 denote the head and the tail indices, respectively, of the 7t span, and T; corresponds the label assigned the respective span.
Multi-word compounds in various languages involve adjacent lexemes juxtaposed with poten- tial semantic relations. In contrast, Sanskrit com- pounds are intuitively constructed based on the semantic compatibility of their components. Ad- ditionally, in Sanskrit, compounds always appear as single word, requiring mandatory sandhi (eu- phonic transformations) between their components. Conversely, certain categories of MWEs (idioms, complex function words, verb-particles, and light- verbs) have relatively fixed structures, predomi- nantly with components separated by spaces. Fur- thermore, nesting within multi-word expressions is considered syntactic overlaps, while the nested structures of Sanskrit compounds result from suc- cessive combinations of components based on mantic relations, thereby clearly distinguishing Sanskrit compounds from MWEs.
How different is NeCTI compared to the Nested Named Entity Recognition (NNER) task? NNER is component of information extraction that aims identify and classify nested named entities within unstructured text, considering their hierarchical structure. In contrast, the NeCTI task focuses on identifying nested spans within a multi- component compound and decoding their implicit semantic relations. These tasks have several key differences: (1) NeCTI operates at the inrd level, whereas NNER operates at the inter-word level, considering entities across a phrase. (2) In NeCTI, multi-component compound can have multiple possible parses, requiring disambiguation through contextual cues and incorporating insights from Päninīan grammar to address incompatibil-
Why NeCTI as a Dependency Parsing Task?
The decision formulate NeCTI dependency parsing task is driven by several considerations. Compounds with more than two components are typically formed through successive binary combi- nations, following specific semantic relations. This

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Compound Root
Global
Relation
Endocentric
Endocentric
Global Relation
Global Relation
sumitra
änanda
vardhanah
laksmanah
råmam
anujagāma
Global
hananda
Hyarchansn
h (kkamanar
harujggama
h Gloosl
Bi-LSTM (5)
XLMA B%
>
Word Embedding
f= IW C
Char Embedding
WORD PIECES
W
C
S
Span Encoding
Word Piece
Bi-LSTM
XLMR
ENCODER
ENCODER
XUMR
sumitrå
änanda
vardhanah
laksmanah
rämam
anujagāma
Global
lakşmanah rämam anujagāma" (Translation: "Lakşmana, the one who enhanced the delight of Sumiträ, followed Rāma") where 'sumitrā- änanda- vardhanafy' is multi-comppoent compound word. We assume prior knowledge of compound segmentation and treat the individual components of multii-componet compounds separate words. That means the compound and its components are known apriori. However, the associations of the components. i.e. spans, are not known apriori. We propose two variants of DepNeCT1 depending on the choice of encoder: DepNeCTI-LSTM and DepNeCTT-XLLB -LMMR. To inform DepNeCTI-LSTT ---M about compound (highlighted with color) and non-compound (highlighted with color) tokens, we employ span encoding. The span encoding uses two randomly initialized vectors (compound non-compound) inform the model whether the corresponding instance is compound or non-compound word. On the other hand, DepNeCTI-XL.N XLMM is informed about compound's location in the input string using bracketing (for example, <sumiträ Ssumiträ-änandi the compound-level parsing task into standard dependency parsing, "Global" color) as global head for all words in the sentence. (2) The compound head and non-compound words are connected to the Global token using the "Compound Root" and "Global Relation" relations, respectively. The hidden representations of the tokens are obtained using Bi-LSTM or XLMR encoder. Finally, Bi-affine (Dozat and Manning, 2017) dependency module applied on top of the hidden representations.
Figure 4: Mustrtationn of DepNeCTI with example saterrr--innna--aaariae
ainada-varddhaaah) vardhanah>> and it lacks span encoding component. Next, to transform
introduce (1) additional token called
process creates nested structure of binary com- pounds, except for few exceptions, where the structure represents a constituency tree. However, treating this as constituency parsing task poses challenges. The nested structure of compounds does not adhere to a syntactic structure but instead follows a semantic structure based on component relations. If we substitute the intermediate nodes with semantic relations, the same spans can be represented as dependency structures by annotat- ing the types as directed relations. Moreover, the headwords in constituency spans are not explicitly marked but can be identified through their corre- sponding types. In contrast, dependency structures allow the determination of the headword based on labels directed towards it within the compound. Notably, dependency structures faithfully capture
constituency information and can be mutually con- verted with their corresponding spans (Goyal and Kulkarni, 2014).
Summarily, the semantic relations among com- pound components resemble dependency relations, which can be represented as directed labels within the dependency parse structure. This approach suc- cinctly represents the semantic relations without introducing intermediary nodes. Lastly, it enables the simultaneous identification of the structure constituency span alongside the identification of compound types. We encourage readers to refer Appendix $ A for more detailed illustration.
4 DepNeCTI: The Proposed Framework
Figure 4 illustrates the proposed framework with an example commträ--nandee ananda-vardhanaal laksmanah

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ramam anujagāma" (Translation: "Laksmana, the one who enhanced the delight of Sumitrā, fol- lowed Râma") where aatrta--nnnnn-avadhaaaaa is colti-component compound word. We as- sume prior knowledge compound segmenta- tion and treat the individual components of multi- component compounds as separate words. We propose two variants of DepNeCTI depending the choice of encoder: DepNeCTI-LSTM and DeLM In DepNeCTI-LSTM, to dif- ferentiate between compound (highlighted with color) and non-compound (highlighted with color) tokens, we employ span encoding. The span encoding uses two randomly initialized vec- tors (compound or non-compound) to inform the model whether the corresponding instance is a com- pound non-compound word. On the other hand, DepNeCT1-XLMR is informed about compound's location the imput string using bracketing (for ex- ample, iatitir---nnnd--vvddia
more than two components (n 2), while also considering binary compounds if they occur in the context. These datasets comprise segmented com- pound components, nested spans, context, and se- mantic relations among the nested spans. We offer two levels of annotations for these datasets: coarse (4 broad types) and fine-grained (86 sub-types). There are 4 broad semantic types of compounds: Avyayībhava (Indeclinable), Bahuvrïhi (Exocen- tric), Tatpuruşa (Endocentric) and Dvandva (Cop- ulative). Again, each broader class is divided into multiple subclasss, leading to 86 fine-grained types. Figure 6 shows class- label frequency in NecTIS fine-grained.
Datasets
#Nested
#Train
#Test
ADev
#TTpps
NeCTIS
17656
12431
3493
2405
(86)
NeCTIS OOD
1189
1189
(86)
Table I: Data statistics for NeCTIS and NeCTIS-OOD
and it lacks
span encoding component. In order to convert the compound-level dependency parsing task into stan- dard dependency parsing, we introduce two mod- ifications. First, we introduce an additional token called "Global" color) which serves the global head for all words in the sentence. Second, tablish connections between the compound head and non-compound words the Global token us- ing the "Compound Root" and "Global Relation" relations, respectively.
Instances Number of components
10
12249
13159
Instances
1366
320
Number of components
Formally, in given sentence X {I1, (21,22,... .... TM with M tokens, let Zp (1 P S M) denote N-component compound. Notably, X may contain multiple occurrences of multi-component compounds. The N-component compound (rp) further split into its components (xp {xr]) zy...., x 1). Next, we pass the overall sequence (X = {21, Ap 22 .... 2n xM)) to the encoder to obtain hidden representations. The LSTM encoder concatenates token's word, character and span embedding obtain its repre- sentation and the XLMR encoder uses word-pieces. Finally, Bi-affine (Dozat and Manning, 2017) dependency module applied on top of it.
Figure 5: Frequency of n-componeet compounds.
Frequency vs Fine-grain Label
20t
207
104
109
Fie-grained labels (decreasing order of frequency)
Figure 6: Frequency in NeCTIS fine-grained.
We introduce two context-sensitive datasets: NeCTIS and NeCTIS OOD. The purpose of the additional dataset (NeCTIS OOD) is create an out-of-domain testbed. The multi-component com- pound instances are extracted from various books
5 Experiments
5.1 Datasets
Table presents data on the total number of multi- component compounds (Figure 5) and their statis-
'The list of fine-grained labels and the cor- responding examples can be found at: https: //sanskrit. uohyd, ac. in. sct//OL/ DATA/Tagging. Guidelines/samaeee sacaasss_atggggn taggingt 6mar12-mooe 2-mmddiieeddpd
tics. Our primary focus is on compounds with

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categorized into 4 types based on subject content: philosophical, paurânic (Translation: Epic is genre of ancient Indian literature encompassing historical stories, traditions, and legends.), literary, and äyurvedā. The NeCTIS dataset encompasses compounds from books falling under the Philosoph- ical, Literary, and äyurvedā categories. The multi- component compound instances extracted from the paurânic category are included in the NeCTIS out-of-domain (NeCTIS OOD) dataset. Further- more, the multi-component instances in NeCTIS belong to the prose domain, while NeCTIS- -OOD pertains to the poetry domain. Poetry commonly uses multi-component compounding extensively (more exocentric compounds) to adhere to metrical constraints and convey complex concepts. Con- versely, prose uses compounds in more direct and less condensed manner. Furthermore, poets in the realm of poetry often enjoy the freedom form novel compounds or employ unconventional ones to conform to meter requirements, rendering these compounds infrequent in regular usage.
of the texts, that discuss the semantics associated with the compounds, which are typically studied by students as part of their coursework. Given these considerations, is very unlikely for professional linguists, often professors instructing these texts, to make mistakes. The dataset was curated around 12 years ago, primarily with the aim of producing freer-free gold-standard data. Consequently, the errors made by junior annotators were not recorded or measured, aligning with our focus on achieving error-free quality. The benchmark for determining correctness was based on the Päninīan grammar.
5.2 Baselines
We investigate the efficacy of various standard for- mulations (originally proposed for nested named entity recognition for English) for the proposed task. We adapt these systems to the NeCTI task by providing the location of the compounds ensure a fair comparison with DepNeCTI. Since these base- lines are leveraged from the nested named entity recognition task, they do not have explicit chan- nels to provide the location of a compound word. Therefore, we provide this information in the input string itself with the help of brackets (for example, ssumirr-ananda-vvrdhhae
We established a data creation process to address the unavailabil- ity of annotated context-sensitive multi-comppnent compound data in Sanskrit. We employ a suffi- cient annotation budget sponsored by DeitY, 2009- 2012 for the Sanskrit Hindi Machine Translation project to employ 6 institutes, each consisting of approximately 10 team members. Each team was organized in a hierarchical manner. There were 3 levels in the hierarchy: Junior linguist (Mas- ters degree in Sanskrit), Senior linguist (Ph.D. in Sanskrit) and professional linguist (Professor in Sanskrit). The annotations from lower expertise were further checked as per the above-mentioned hierarchy. Subsequently, the annotated data un- derwent an exchange process with another team for correctness verification. Any ambiguities en- countered during the annotation process were re- solved through collective discussions conducted by the correctness-checkingg team. The available books were distributed among these teams, and each team responsible for annotating their allo- cated books. The annotation guidelines are essen- tially based on Sanskrit grammar which provides the syntactic and semantic criteria for annotation. Elaborate commentaries accompany the majority
Dataset Annotation Process:
Constitueney Parsing (CP): Following Fu al. (2020), we formulate NeCTI as constituency pars- ing with partially-observed trees, with all labeled compound spans as observed nodes in a con- stituency tree and non-compound spans as latent nodes. We leverage TreeCRF to model the ob- served and the latent nodes jointly.
Bottom-up Constituency Parsing (BotCP): Fol- lowing Yang and Tu (2021), we formulate NeCTI a bottom-up constituency parsing, where a pointer network leveraged for post-order traver- sal within a constituency tree to enhance parsing efficiency, enabling linear order parsing.
Span Classifier (SpanCL): Following Yuan et al. (2022), we formulate NeCTI as a span classifi- cation problem, where triaffine mechanism is leveraged to learn a better span representation by integrating factors such inside tokens, bound- aries, labels, and related spans.
Lexicalized Constituency Parsing (LexCP): Following Lou et (2022), we formulate NeCTI as lexicalized constituency parsing, which em- beds a constituency and a dependency trees
'The guidelines are available at: https://sansker uohyd. ac. in/scl/GOLD BooooDDDDT/TTTgginnnno leaeiingGGiieeeees// samaasa_tage taggingi 6mar1 6mar12-modi fidd. pdd

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Coarse
Fine Grain
w/o context
context
w/o context
context
Models
USS
LSS
EM
USS
LSS
EM
USS
LSS
EM
USS
LSS
EM
BotCP
72.90
58.78
32.26
76.22
63.97
35.10
74.28
33.50
18.58
75.72
41.80
23.05
76.83
61.71
39.97
46.27
30.14
77.38
41.86
27.22
LexCP
93.39
84.74
72.88
93.39
85.16
74.41
88.70
45.86
14.72
87.86
48.87
19.67
NeCTIS
Seg2seq
92.34
84.11
59.90
91.18
80.45
52.89
92.67
65.63
30.65
92.94
68.19
34.35
SpanCL
92.84
84.80
69.12
93.13
84.74
69.67
93.12
69.38
52.17
92.69
68.05
50.82
DepNeCTI-LSTM
95.46
89.06
76.82
97.42
89.24
77.00
95.38
79.49
57.46
97.49
79.72
56.83
DepNeCTI-XI.MR
96.21
90.83
79.85
96.16
90.67
79.45
96.35
83.36
63.92
96.34
83.19
63.30
BotCP
72.81
48.08
21.63
73.89
22.57
72.30
20.52
8.64
13.32
CP
71.43
52,65
34.57
68.17
38.63
25.15
76.12
29.68
19.77
69.57
24.10
15,43
LexCP
89.90
71.44
50.00
91.57
72.60
52.73
83.45
32.85
7.53
83.38
34.18
8.31
NeCTIS OOD
Seq2Scq
84.26
71.71
45.33
90.13
71.41
44.00
92.16
53.55
24.51
92.87
53.61
24.96
SpanCI
91.51
72.69
56.30
90.24
71.89
54.44 46..66
89.19
50.21
32.66
90.88
50,50
34.00
DepNeCTI-LSTM
93.32
78.94
57.40
95.67
79.26
57,90
93.88
67.96
36.60
95.88
67.26
36.26
DePNeCTI-XLMR
95.56
84.24
65.50
95.56
84.45
65.00
95.56
74.26
45.70
95.45
73.54
44.37
Table 2: Evaluation on the NeCTIS datasets, considering 2 levels of annotations (coarse and fine-grained) and in 2 settings (with and without context). The rest-performing results are bold, while the second- best results are underlined. The results are averaged over 3 runs. To assess the significance between the proposed system and the best baselines for each setting, significance test in Accuracy metrics was conducted: < 0.01 (as per (-test).
together. This formulation leverages the con- stituents' heads into the architecture which is crucial for the NeCTI task.
5.3 Results
Table presents the performance of the top- performing configurations among all baselines on the NeCTIS benchmark datasets' test set. The eval- uation includes 2 levels of annotations (coarse and fine- fine-graine grained) and in 2 settings (with and without context). While all baseline systems demonstrate competitiveness,
Seq2Seq: Following Yan al. (2021), we for- mulate NeCTI an entity span sequence genera- tion task using the pretrained seq2seq framework. This generative framework can also identify dis- continuous spans; however, NeCTI does not have such instances.
single baseline consistently outperforms the others across all settings. Conse- quently, we underline the best-performing numbers within each specific setting.
DepNeCTT:: We propose two variants of our system ($4) depending on the choice of en- coders: DepNeCTI-LSTM and DepNeCTI- XLMR (Nguyen et al., 2021). The compound's location is provided in DepNeCTI-LSTM us- ing span encoding: however, DepNeCTI-XLMR lacks span encoding and leverages this informa- tion from the input similar to other baselines.
Our proposed system DepNeCTI surpasses all competing systems across all evaluation metrics, demonstrating an absolute average gain of 13.1 points (LSS) and 1 11.3 points (EM) compared to the best-performing baseline in each setting. No- tably, our proposed system exhibits substantial per- formance superiority over the best baseline in fine- grained settings, particularly in low-resourced sce- narios. This validates the effectiveness our pro- posed system in low-resourced settings with fine- grained labels. The significant performance gap between our proposed system and the best base- lines highlights the efficacy of employing simple yet effective architecture inspired by the depen- dency parsing paradigm. These results establish new state-of-the-arr benchmarks by integrating the contextual component into our novel framework. While most baselines (except BotCP) do not bene- fit from contextual information, DepNeCTI-LSTM
Evaluation Metries: We evaluate the perfor- mance using the Labeled/Unlabeled Span Score (LSS/USS) terms of micro-averaged Fc-rc. We define LSS as a micro-averaged F1-score ap- plied on tuples of predicted spans including their labels. We exclude labels of the spans while calcu- lating USS. Additionally, report the exact match (EM) which indicates the percentage of the com- pounds for which the predictions of all spans and their semantic relations are correctly identified. Re- fer to Appendix for the computing infrastructure used.
and details

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demonstrates slight improvements and DepNeCTI- XLMR shows on par improvements when leverag- ing contextual information. Furthermore, as the number of components grows, the number of po- tential solutions increases exponentially, leading to poor performance by the systems in such scenar- ios. Due to this exponential possibilityy. contextual information provides limited improvements com- pared to binary compound identification settings (Sandhan et al., 2022a). In other words, unlike the context-free setting, the introduction of context information does not warrant an expectation for the system to precisely generate the correct solu- tion from the exponential candidate space. Figure 3 provides a visual representation that elucidates the concept of this exponential candidate space. A similar performance trend is observed for the NeCTIS-OOD dataset.
the number of components in compounds. For compounds with a small number of components, all systems demonstrate high performance, but our proposed systems consistently outperforms other baselines. However, as the number of components increases, the number of examples in each category decreases. Additionally, the number of potential solutions grows exponentially, following the Cata- lan number. Consequently, all systems experience decline in performance.
F1 score vs Number of components
Beity
SAg2So3
e
Dapfiet T1XUL
20-
Number of components
6 Analysis
Figure 7: F1-Score against the number of components, The compounds with components 10 are excluded.
Here, examine the proposed system, focusing on a comprehensive analysis and its applicability. For this purpose, we utilize the NeCTIS coarse dataset under the w/ context configuration. We report LSS in terms of macro-average Fl-scores.
(3) Error analysis: We investigate whether all the systems are able to identify the location of a multi multi-component compound correctly. The motiva- tion behind this experiment is to evaluate the capa- bility of the baselines and the proposed architecture to leverage the information about the compound's s location effectively. We define the span of text that corresponds to a compound as global span which we know apriori. Figure illustrates the effective- ness of each system in correctly identifying the global span of multi-componcet compounds. In
(1) Ablation analysis: In this study, analyze the impact of different system components the overall enhancements of DepNeCTI-LSTM. Ab- lations, documented in Table 3, present the eval- uation metries when a specific component de- activated within DepNeCTI-LSTM. For instance, the absence of the span encoding component is de- noted as "- Span Encoding". The results indicate that removing any component leads to a decline in performance. Notably, Table 3 highlights the significance of the "Span Encoding" component in driving the improvements.
Global match Number
102
Clobal Span match %
100
98
BotCP
SpanCL
System
EM
LexCP
DepNeCTI-LSTM
89.24
89.24
89.24
77.00
92
DepN CTT-L-CT--STMM
DepNicCTI-LLAR
XLMM
FastText (FT)
88.84
88.84
88.84
76.50
2
5
6
8
9
10
Span Encoding (SE)
86.18
85.53
86.85
70.54
Number of components
FT
84.25
84.23
84.24
67.86
Figure 8: Performance the systems on identifying the global span of compound.
Table 3: Each ablation involved the removal of sin-
gular component from DepNeCTI-LSTM. The ablation denoted as "-Span Encoding" entailed eliminating the span encoding component from the proposed system.
DepNeCTI-LSTM, span encoding effectively captures this information, resulting in perfect 100% score. However, even after providing the baselines with this information, they fail use it due to limitations in their architectures. Interest- ingly, DepNeCTI-XLLR does not contain a span
(2) How robust the system when the number of components of a compound increases? We analyze the relationship between the FI-score and

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encoding component and leverages the compound's location information from the input as provided for the baselines. Still, DepNeCTI-XLMR reports the best performance due to its powerful word repre- sentation ability. It is worth noting that the NeC- TIS dataset exhibits an inherent bias towards left branching, as indicated by the nested tree structure. Consequently, all systems display bias towards left branching as well. Therefore, due to the dom- inance of brathbranhhin instances and increased variance due less number of instances, a spike is observed in the results.
2015; Dima and Hinrichs, 2015; Fares et al., 2018;
Shwartz and Waterson, 2018; Ponkiya et al., 2018, 2020, 2021), multi-word expression (MWE) (Con- stant et al., 2017; Gharbieh et al., 2017; Gooding et al., 2020; Premasiri and Ranasinghe, 2022) and named entity recognition (Fu et al., 2020; Yang and Tu, 2021; Lou et al., 2022; Yuan et al., 2022), the nested compounds remains unexplored.
Sanskrit Compound Type Identification has at- tracted significant attention over the past decade. Decoding the meaning of a Sanskrit compound requires determining its constituents (Huet, 2009; Mittal, 2010; Hellwig and Nehrdich, 2018), un- derstanding how these constituents grouped (Kulkarni and Kumar, 2011), identifying the mantic relationship between them (Kumar, 2012), and ultimately generating a paraphrase of the com- pound (Kumar et al., 2009). Previous studies pro- posed rule-based approaches (Satuluri and Kulka- rni, 2013; Kulkarni and Kumar, 2013), data- driven approach (Sandhan et al., 2019) and a hybrid approach (Krishna al., 2016) for SaCTI. Sandhan et al. (2022a) proposed a context-sensitive architec- ture for binary compounds.
(4) Efficiency of our proposed system: Figure 9 present the computational efficiency of our sys- tem measured in terms of the number of sentences processed per second. We compare the inference speed of different baselines the NeCTI task. Our systems, DepNeCTI-LSTM and DepNeCTI- XLMR are leveraging a simple architecture and uti- lizing dependency parsing as an appropriate prob- lem formulation, exhibits a 5- foldd/ improve- ment over the most efficient baseline, BotCP.
Inference speed per Model
Inference speed (sentences/see coccc)/
1420-
Earlier works in Sanskrit solely focused on bi- nary compounds, neglecting the identification of multi-component compound types; however, our proposed framework fills this research gap.
1200
DepNeCT:
430-
8 Conclusion
200
LexOP SpanCL BotCP 2eq Ours Ourse Model
In this work, we focused on multi-component com- pounding in Sanskrit, which helps to decode the implicit structure of a compound to decipher its meaning. While previous approaches have primar- ily focused on binary compounds, we introduced novel task called nested compound type identifi- cation (NeCTI). This task aims to identify nested spans within multi-compones compounds and de- code the implicit semantic relations between them, filling a gap in the field of lexical semantics. To fa- cilitate research in this area, we created 2 newly an- notated datasets, designed explicitly for the NeCTI task. These datasets were utilized to benchmark various problem formulations. Our novel frame- work DepNeCTT outperformed the best baseline system by achieving stupendous absolute gain of 13.1 points F1-score in terms of LSS. Similar the previous findings on binary Sanskrit compound identification, we discovered that our proposed sys- tem exhibits substantial performance superiority over the best baseline in rewoureddd scenarios.
Figure 9: Inference speed of the competing systems.
7 Related Works
Lexical semantics is dedicated field focused on word meaning. Various tasks such word-sense disambiguation (Bevilacqua and Navigli, 2020; Barba al., 2021; Maurya al., 2023; Maurya and Bahadur, 2022), relationship extraction (Tian et al., 2021; Nadgeri et al., 2021; Hu et al., 2022; Wang et al., 2022), and semantic role labeling (He et al., 2018; Kulkamn and Sharma, 2019; Zhang et al., 2022) play essential roles in determining word meaning. Morcover, when dealing with com- plex word structures such compounds, named entities, and multi-word expressions, relying solely on basic word senses and relationships inad- equate. While efforts have been made in Noun Compound Identification (Ziering and van der Plas,

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Limitations
References
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We could not extend our framework other lan- guages exhibiting multi-component compounding phenomena due to the lack of availability of anno- tated datasets. It would be interesting to measure the effectiveness of rules from Päninīan grammar to discard incompatible semantic relations (Kulkarni, 2019, 2021).
Edoardo Barba, Luigi Procopio, and Roberto Navigli. 2021. ConSeC: Word sense disambiguation
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Ethics Statement
This work introduces a new task, along with an- notated datasets and a framework, to address the nested compounding phenomenon in Sanskrit. The proposed resources aim to enhance the understand- ing of multi-component compounds and contribute the improvement of machine translation sys- tems. Regarding potential effects, we anticipate no harm to any community resulting from the use of our datasets and framework. However, we ad- vise users to exercise caution, as our system is not flawless and may generate mispredictions. To en- sure transparency and future research, we have pub- licly released all our annotated NeCTIS datasets and source codes. We confirm that our data col- lection adheres to the terms of use of the sources and respects the intellectual property and privacy rights of the original authors. Our annotation team consisted of qualified individuals, including Mas- ter's and Ph.D. degree holders, some of whom are Sanskrit professors. Annotators were compensated appropriately and provided with detailed instruc- tions to ensure consistency in the annotation pro- cess. We remain committed to addressing ethical implications as we refine our systems and welcome feedback from the community to enhance our ethi- cal practices.
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A Acknowledgements
We thank all the annotators from different institutes for helping us with NeCTIS data annotation. We would like to thank Zheng Yuan, Tsinghua Uni- versity (Alibaba Group), for helping us with the hyperparameter section and adapting his system for the NeCTI task. We appreciate and thank all the anonymous reviewers for their constructive feed- back towards improving this work. The work was supported in part by the National Language Trans- lation Mission (NLTM): Bhashini project by Gov- emmment of India.
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A Why NeCTI Dependency Parsing Task?
Compounds language are semantic construc- tions. While a limited number rules derived from Sanskrit Grammar aid in determining the syn- tactic structure of a compound, they offer limited assistance in uncovering its meaning. The mean- ing of compound primarily arises from the mantic relationship between its components. This semantic relationship. known as "sämarthya," is expressed through various types of semantic com- pounds. These compound types also facilitate the identification of the headword within a compound. The hcadword can be one of the constituents or an entirely distinct word modified by the resulting compound. Compounds consisting of more than two components are typically formed through suc- cessive binary combinations of the components, following specific relations. a result, nested structure of binary compounds created, with a few exceptions. Therefore, the semantic compound types play a vital role in determining the correct nested structure amidst the various possible struc- tures of compound. Each of these structures rep- resents constituency span, and parser equipped with compound type identification assists in curately identifying the appropriate constituency span.
Patrick Ziering and Lonneke van der Plas. 2015. From a distance: Using cross-lingual word alignments for noun compound bracketing. In Proceedings of the 11th International Conference on Computational Se- mantics, pages 82-87, London, UK. Association for Computational Linguistics.
Treating this as constituency parsing task presents several challenges. Figure 10 illustrates the potential constituency spans for a 3-component compound b c, with ab and be representing the intermediate compounds. First, the nested struc- ture of compounds does not conform to a syntactic structure; instead, follows a scmantic structure based on the relations between the components rather than their position or relative co-occurrence. Second, the intermediate compounds within the structure are not categories but stem forms. Each intermediate compound serves modifies the meanings of one of its constituents, subsequently combining with another component to form a larger compound.
entity that
Kumar (2012) formulated this constituency parsing, where the identification of semantic com- patibility performed based on the relative co- occurrence and relative position of the compo- nents. As shown in Figure 11, the intermediate compounds, which lacked additional information, were substituted with their respective semantic compound types in the type identification stage.

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head word can be determined by the labels directed towards it within the compound. Notably, these dependency structures faithfully capture the con- stituency information and are mutually convertible with their corresponding constituency spans.
abc
abc
ab
a
bc
c
(4) < <a-bb-c>>
(b) < a <h-
(a) << a b> T1
T2
Figure 10: Possible constituency spans for three com- ponent compound a-b-c
T4
T3
a
T2
T4
(b) < <
T3 T4
Figure 12: Dependency Structure for the three compo- nent compound a-b-c with semantic types as labels
T1
a
T3
There are several considerations behind the de- cision treat NeCTI as dependency parsing task. First, the semantic relations among com- pound components resemble dependency relations and can be represented as directed labels within the dependency parse structure. Second, this ap- proach concisely represents the semantic relations between compound components without introduc- ing extra intermediary nodes. Lastly, it enables the simultaneous identification of the structure or constituency span alongside the identification of compound types.
b
(a) < <x- b> T2
(b) <a <b e T3>
Figure I1: Possible constituency spans for three com-
ponent compound a-b-c with semantic types
The semantic types serve as semantic construc- tions for compounds, rather than being syntactic categories and play crucial role in determining the meaning of the compound. For example the com-- pound vidyälayaghantä has the nested structure (artds--llayaTTT-ghhaa 6-ghnma>T66. The type T6 indi- cates saşthi taturrua compound (6th case determi- native compound) inferring a possessive relation- ship. Vidyā (knowledge) and ālaya (place) combine to form the intermediate compound vidyalaya (the place of knowledge, viz. school), which combines with ghanjä (bell) to form the whole compound indicating school-bell. The possessive relation ex- pressed by the compound type (T6) is akin dependency relation, and this holds true for other compound types as well.
B Experiment Details
Hyper-parameters: For our proposed system, we build on the top of the codebase from BiAFF, as developed by Ma et al. (2018). We configure the hyperparameters as follows: batch size of 16, 100 training iterations, dropout rate of 0.33, stacked Bi-LSTM layers, learning rate of 0.002, and the remaining parameters set identically to those used in the work of Ma al. (2018). We use man- ual tuning for the hyper-parameter selection and F1-score criteria on dev set's performance Our codebase is publicly available and released under creative-commot license. We use FastText word embeddings for the proposed framework.
Consequently, when represented with compound types, the same constituency spans can be depicted dependency structures by annotating the types directed relations and removing the extra nodes indicating the types. The dependency structures for the previous example (a- in Figure 12. Furthermore, the head words are not explicitly marked in constituency spans and can only be identified through their corresponding types. However, with dependency structures, the
Computing Infrastructure Used: We perform our experiments using a single GPU equipped with an NVIDIA A40, 48 GB GPU memory, and 10752 CUDA cores. We employ a single GPU with an NVIDIA Quadro RTX 4000, 8 GB GPU memory, and 2304 CUDA cores for our proposed system.
e) shown


Reviews:
[{"review_id": "3JQDdB9PWZ", "reviewer": "Reviewer_TGub", "summary": "", "questions": "\nline 251, how do you define \"intermediate nodes\"? I also do not understand the sentence here (\"are stem forms, not categories\").\nWhat is a \"category\"? I don't think this has been mentioned previously.\n\nline 287, would you care to explain what you mean by one-hot span encoding? What are you exactly encoding? \n\nline 320-321, please explain the difference between \"coarse\" and \"fine-grained\" annotations. Also please provide examples.\n\nline 328, what is \"pauranic\"? While \"ayurveda\" can be assumed to be understood by most english speakers, the word \"pauranic\"\nhas not yet entered common knowledge.\n\nFigure 7: can you add a note to try to explain the unexpected spike for length 10?\n\nSection 7 on \"Lexical Semantics\": I am not sure much of this is really relevant. Can you motivate it better?\n\n\n", "limitations": "", "rating": 3, "confidence": 4, "soundness": 3, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "We appreciate your recognition of our dataset and dependency-based framework contributions. It is pleasing that you noted the well-designed work, clear computational writeup, compliant experiments, and substantial performance gain by our framework over SOTA baselines.\n>_I find that the authors could have defined their tasks more broadly. Why restricting it to Sanskrit? There are many languages that exhibit productive compounding (e.g. German) so the task could be defined as \"Nested Compound Type Identification\" in a language-agnostic way._\n\nIndeed, our proposed task is language agnostic. Our dedicated section: Problem Formulation, defines the task formally. It does not leverage any Sanskrit-specific terminology. We have shown its application in the Sanskrit language. Therefore, we call the task nested compound type identification for Sanskrit (NeCTIS). Perhaps, it might be confusing due to the acronym (NeCTIS). We will rephrase it as NeCTI in our final draft version to avoid confusion. \n\n> _It is true that it might be difficult suitably annotated corpora, but perhaps existing corpora of compounds can be adapted for the task._\n\nWe want to reiterate that the proposed task is not a classification task. Rather, it is a word-level structured prediction task involving the identification of spans that adhere to defined constraints (with valid solutions representing complete parenthesization) and their corresponding semantic relationships. As far as our understanding goes, there is currently no annotated dataset accessible for languages demonstrating compound phenomena that encompass spans and linked semantic labels. We had mentioned this in the limitation section (Line 579-582): “We could not extend our framework to other languages exhibiting multi-component compounding phenomena due to the lack of availability of annotated datasets.” \n\n> _For example a simple search for \"German noun compounds corpus\" leads me to the following_ https://www.ims.uni-stuttgart.de/forschung/ressourcen/lexika/ghost-nn/ \n\nThis German compound dataset explicitly focuses on binary compounds with 6 semantic classes. This dataset can not serve as a testbed for our task, as per our previous response. We had done a thorough survey to find potential datasets for the languages exhibiting multi-component compounding phenomenon. However, we could not find such a dataset. We had acknowledged this in our limitation section: “We could not extend our framework to other languages exhibiting multi-component compounding phenomena due to the lack of availability of annotated datasets.”\n\n---\n\n`Response for questions to authors`\n> _line 251, how do you define \"intermediate nodes\"? I also do not understand the sentence here (\"are stem forms, not categories\"). What is a \"category\"? I don't think this has been mentioned previously._\n\nDue to space constraints, we could not talk about it elaborately. However, at the end of the same section, as per our note (Line 272-274: “We encourage readers to refer to Appendix § A for a more detailed illustration.”), we had given detailed explanations with illustrative examples on it.  Lines 916-923: “Figure 10 illustrates the potential constituency spans for a 3-component compound a−b−c, with ab and bc representing the intermediate compounds. First, the nested structure of compounds does not conform to a syntactic structure; instead, it follows a semantic structure based on the relations between the components rather than their position or relative co-occurrence.” Therefore, it would be helpful if we substitute intermediate nodes ab and bc with their semantic categories. Lines 934-937: “As shown in Figure 11, the intermediate compounds, which lacked additional information, were substituted with their respective semantic compound types in the type identification stage.” We encourage the reviewer to see Figure 10-11 to get a better visualization of it.\n\n> _line 287, would you care to explain what you mean by one-hot span encoding? What are you exactly encoding?_\n\nThanks for pointing it out. To add more clarity, we will update the final version with the following note: “The span encoding uses two randomly initialized vectors (compound or non-compound) to inform model whether the corresponding instance is compound or non-compound word.” We will rephrase one-hot span encoding as span encoding in our final draft version.\n\n> _line 320-321, please explain the difference between \"coarse\" and \"fine-grained\" annotations. Also please provide examples._\n\nWe had briefly clarified the key difference in the line 319-321: “We offer two levels of annotations for these datasets: coarse (4 broad types) and fine-grained (86 sub-types).” There are 4 broad semantic types of compounds: Avyayībhava (Indeclinable), Bahuvrīhi (Exocentric), Tatpuruṣa (Endocentric) and Dvandva (Copulative). Again, each broader class is divided into multiple subclasses, leading to 86 fine-grained types. We will update the list of fine-grained labels and the examples in our final draft in the Appendix.\n\n> _line 328, what is \"pauranic\"? While \"ayurveda\" can be assumed to be understood by most english speakers, the word \"pauranic\" has not yet entered common knowledge._\n\nThanks for pointing it out. We will  update the final draft version with its English translation: Epic. Epic is a genre of ancient Indian literature encompassing historical stories, traditions, and legends.\n\n> _Figure 7: can you add a note to try to explain the unexpected spike for length 10?_\n\nWe had mentioned in lines 471-472 :”As the number of components increases, the number of examples in each category decreases.” In lines 488-491 “The NeCTIS dataset exhibits an inherent bias towards left branching, as indicated by the nested tree structure. Consequently, all systems display a bias towards left-branching as well.” Therefore, due to the dominance of left-branching instances and increased variance due to less number of instances, a spike is observed in the results. As per your suggestion, we will add this note in our final version of the draft.\n\n> _Section 7 on \"Lexical Semantics\": I am not sure much of this is really relevant. Can you motivate it better?_\n\nWe had mentioned in line 509-510: ”Lexical semantics is a dedicated field focused on word meaning.” In abstract (line 12-13), we had claimed: “To the best of our knowledge, this is the first attempt in the field of lexical semantics to propose this task.” This motivation aimed to contextualize our work within the larger scope of this field, highlighting our contribution to its advancement.", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "\nWell designed study. Significant effort has been put into the creation of the corpus, a process which has involved multiple institutions and several Sanskrit experts. The computational part is clearly described, and the experiments are within the standards of the community, using state-of-the-art baselines, and improving upon them.", "reasons_to_reject": "\nI find that the authors could have defined their tasks more broadly. Why restricting it to Sanskrit? There are many languages that exhibit productive compounding (e.g. German) so the task could be defined as \"Nested Compound Type Identification\" in a language-agnostic\nway. It is true that it might be difficult suitably annotated corpora, but perhaps existing corpora of compounds can be adapted for the\ntask. For example a simple search for \"German noun compounds corpus\" leads me to the following\n\nhttps://www.ims.uni-stuttgart.de/forschung/ressourcen/lexika/ghost-nn/\n\nThere are also some parts of the paper that in my opinion would benefit if more details were provided, see notes below.", "questions_to_authors": "\nline 251, how do you define \"intermediate nodes\"? I also do not understand the sentence here (\"are stem forms, not categories\").\nWhat is a \"category\"? I don't think this has been mentioned previously.\n\nline 287, would you care to explain what you mean by one-hot span encoding? What are you exactly encoding? \n\nline 320-321, please explain the difference between \"coarse\" and \"fine-grained\" annotations. Also please provide examples.\n\nline 328, what is \"pauranic\"? While \"ayurveda\" can be assumed to be understood by most english speakers, the word \"pauranic\"\nhas not yet entered common knowledge.\n\nFigure 7: can you add a note to try to explain the unexpected spike for length 10?\n\nSection 7 on \"Lexical Semantics\": I am not sure much of this is really relevant. Can you motivate it better?\n\n\n", "paper_topic_and_main_contributions": "The paper is about an approach that enables the correct interpretation of compound words in Sanskrit, by creating dependency trees over the components of the compound. The authors have created a rich corpus of annotated compounds, which they use in their study.\nThus the paper offers two contributions: the annotated corpus and the method for dependency-based disambiguation of the inner structure of the compound.", "excitement": 3, "reproducibility": 3, "ethical_concerns": "No", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {}, "extra_text": {}}, {"review_id": "sBPVixeGRL", "reviewer": "Reviewer_7qSR", "summary": "", "questions": "a) Is there a good reason to use nested name entity recognition systems proposed for English as a baseline? ", "limitations": "", "rating": 2, "confidence": 2, "soundness": 3, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "We are encouraged you appreciated our dataset contribution, its benchmarking and our novel dependency-based framework along with significant improvement over strong baselines.\n\n> _The paper would benefit from further proofreading. At times, it is unclear._\n\nWe had proofread but there is chance we might have missed some things. It would have greatly assisted if the reviewer could have specifically pointed out his concerns like grammatical errors, missing references, spelling errors etc.\n\n> _“It is not clear why the authors have created the two datasets.”_\n\nThe dataset section (Lines 324-337) had clarified the purpose of the additional dataset was to create an out-of-domain testbed. It is also self-explanatory from their names: NeCTIS and NeCTIS OOD. Lines 324-337: “We introduce two context-sensitive datasets: NeCTIS and NeCTIS OOD. The multi-component compound instances are extracted from various books and categorized into 4 types based on subject content: philosophical, pauranic, literary, and ayurveda. The NeCTIS dataset encompasses compounds from books falling under the Philosophical, Literary, and Ayurveda categories. The multi-component compound instances extracted from the pauranic category are included in the NeCTIS out-of-domain (NeCTIS OOD) dataset. Furthermore, the multi-component instances in NeCTIS belong to the prose domain, while NeCTIS-OOD pertains to the poetry domain.”\n\n> _The two datasets are presumably separated by the level of annotation ('coarse' and 'fine') but what this actually means is not explained._\n\nNot exactly! Referring to our earlier response, we have two datasets- NeCTIS and out-of-domain NeCTIS-OOD. For each dataset, we have two levels of annotations, namely coarse and fine-grained. We had clarified this in the dataset section line 320-321: “We offer two levels of annotations for these datasets: coarse (4 broad types) and fine-grained (86 sub-types).” \n\n> _I understand time and resource constraints not permitting inter-annotator agreement, but \"diverse pool of annotators, including experts\" does not inspire the intended confidence for data quality. Given the complexity of multi-component compounds the authors stress, this statement seems like a dismissal._\n\nWe could not elaborate much on it due to space constraints. Lines 342-343 state: “We employ 6 institutes, each consisting of approximately 10 team members.” In each team, the diverse pool set was used in a hierarchical manner. There were 3 levels in the hierarchy: Junior linguist (Master of Art in Sanskrit), Senior linguist (Ph.D. in Sanskrit) and professional linguist (Professor in Sanskrit). The annotations from lower expertise were further checked as per the above-mentioned hierarchy. Line 346-351: “Subsequently, the annotated data underwent an exchange process with another team for correctness verification. Any ambiguities encountered during the annotation process were resolved through collective discussions conducted by the correctness-checking team.” The annotation guidelines are essentially based on Sanskrit grammar which provides the syntactic and semantic criteria for annotation. Elaborate commentaries accompany the majority of the texts, that discuss the semantics associated with the compounds, which are typically studied by students as a part of their coursework.  Given these considerations, it is very unlikely for professional linguists, often professors instructing these texts, to make mistakes.The dataset was curated around 12 years ago, primarily with the aim of producing error-free gold-standard data. Consequently, the errors made by junior annotators were not recorded or measured, aligning with our focus on achieving error-free quality.\n\n***\n\n`Response for questions to author`\n> _Is there a good reason to use nested name entity recognition systems proposed for English as a baseline?_\n\nAbsolutely! We have introduced a novel task – the nested compound type identification. As far as current knowledge stands, no pre-established benchmarks are available in any language. A parallel can be drawn with the task of nested named entity recognition (NER), which bears similarity to our proposed task. Consequently, it becomes a logical and straightforward decision to repurpose the existing NER baselines for our task. It is important to note that these baselines, initially formulated for English, hold language-agnostic qualities and are not inherently tailored to English alone. While the Nested NER task has predominantly revolved around the English language, evaluating these baselines has been primarily centered on English due to its prevalence in this domain. In light of this, it would have greatly assisted if the reviewer could have suggested alternatives superior to the ones we have employed as our baselines.\n***\n`Typos Grammar Style And Presentation Improvements`\n\n> _It is unclear to what T6 refers to in figure 1 and 2, as it's not explained in text or in caption._\n\nWe had given its English label in the corresponding figures. For example, Endocentric (T6).\n\n> _line103 & line116 the section references do not seem to match the corresponding referents._\n\nThanks. We will rectify this in the final version of the draft.\n\n> _Axes labels in Figure 6._\n\nWe are not sure what exactly the reviewer wants to convey. We feel that axes labels are self-explanatory: The fine-grained labels have been put in decreasing order of frequency on the X-axis (and the label mentions it), while the Y-axis shows the frequency of the corresponding label (and the label mentions it). It was not possible to write the label names on the X-axis, as there are 86 labels.", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "The work and dataset extend on existing literature by incorporating more complex phenomena, namely multi-component compounding rather than two component compounds. ", "reasons_to_reject": "The paper would benefit from further proof reading. At times it is unclear. \n\nIt is not clear why the authors have created the two datasets. The two datasets are presumably separated by the level of annotation ('coarse' and 'fine') but what this actually means is not explained. \n\nI understand time and resource constraints not permitting inter-annotator agreement, but \"diverse pool of annotators, including experts\" does not inspire the intended confidence for data quality. Given the complexity of multi-component compounds the authors stress, this statement seems like a dismissal. \n\n", "questions_to_authors": "a) Is there a good reason to use nested name entity recognition systems proposed for English as a baseline? ", "paper_topic_and_main_contributions": "This paper presents two datasets for multi-component compounds in Sanskrit. The datasets are used to provide benchmarks for existing approaches to Noun compounding in Sanskirt. The authors propose a dependency parsing-like approach, which yields significant improvement in model performance\n \n ", "excitement": 2, "reproducibility": 4, "ethical_concerns": "No", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {}, "extra_text": {"Typos_Grammar_Style_and_Presentation_Improvements": "It is unclear to what T6 refers to in figure 1 and 2, as it's not explained in text or in caption. \n\nline103 & line116 the section references do not seem to match the corresponding referents. \n\nAxes labels in Figure 6. "}}, {"review_id": "wAj7ukyGMY", "reviewer": "Reviewer_8vVf", "summary": "", "questions": "1. Confusion regarding span encoding,\n    - The proposed method assumes span boundaries are given (lines 281–287)\n    - however, the text in line 427 incorrectly suggests the system ‘predicts’ the spans\n    - also, text in line 482—485 suggest accurate identification of spans.\n    - If I understand correctly, the model assumes the gold span information is already available in the input (one-hot span encoding). If so, it's an unfair comparison in Figure 8.\n2. Lines 442—445 incorrectly suggest baselines don’t gain from the context. It should be rephrased to indicate BotCP benefits from the context.\n3. What guidelines were provided to the annotators? Consider including them in the appendix.\n4. Diverse pool of annotators actually highlights the need for measuring inter-annotator agreement. While I understand many of them could be experts, IAA can vary based on the provided annotation guidelines.\n5. Is the dataset transliterated? If not, what models are used for word embeddings?\n6. Evaluation metrics\n    - What are the standard metrics for binary compound identification task? Are they P, R, F1, and EM on span tuples? It would be useful to provide references to prior work.\n    - If the dependency formulation is the most appropriate, why not use standard dependency parsing metrics such as unlabeled and labeled attachment scores.\n7. Include examples of coarse and fine-grained types.\n8. Any thoughts on why the context doesn’t help the proposed method?\n9. What are some qualitative differences in compounds in prose vs poetry text?", "limitations": "", "rating": 4, "confidence": 3, "soundness": 4, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "We are really glad to see your thorough review with intriguing questions. We acknowledge your efforts to review our work. We appreciate your acknowledgment of our well-motivated work, comprehensive comparison with nested NER and MWE detection, a thoughtfully curated dataset encompassing an out-of-domain split, benchmarking, and a thorough comparison of different formulations, notably our innovative dependency parsing-based approach.\n\n> _\"The formulation of the task as a dependency parsing problem is not convincing to me. In Figure 4, there are many dependency relations (global relation) and additional tokens (global token) that don't seem to directly help solve the task.\"_\n\nThanks for raising this concern. We interpret it as an implicit recognition of the novelty we have introduced through our dependency parsing formulation. When the reviewer mentions it is \"not convincing,\" we understand that it is not obvious or novel. Our novelty involves transforming compound parsing into standard dependency parsing. This enables the use of any off-the-shelf dependency parsers, like the Bi-affine model (Dozat and Manning, 2017), to solve this task. To facilitate this, we introduced the above-mentioned modifications. We had clarified this in the caption of Figure 4 and lines 288-296: “In order to convert the compound-level dependency parsing task into standard dependency parsing, we introduce two modifications …”\n\n> _\"Given that the context is not helpful empirically\"_\n\nNot exactly! Lines 442-445 “While most baselines do not benefit from contextual information, our system demonstrates slight improvements when leveraging contextual information.” Considering the exponential solution space, our framework exhibits a slight enhancement in the with-context setting over without-context setting.\n\n> _\"Can we consider a formulation where only the tokens in the compounds constitute the input? This reduces the label space and makes the system more efficient.\"_\n\nIndeed, we had already considered without-context setting. It is mentioned in Table 2’s caption and lines 414-417: “Evaluation on the NeCTIS datasets, considering 2 levels of annotations (coarse and fine-grained) and in 2 settings (with and without context).”\n\n> _\"The paper can benefit from additional details on dataset collection, and baseline description\"_\n\nWe had provided the necessary details regarding dataset collection. In this regard, please find our response below. As per your suggestion, we will update this additional information (annotation guidelines etc.) in the Appendix. Further, we have clarified in our response that the description of the proposed framework contained all the essential details to resolve the concerns raised by the reviewer.\n\n***\n`Response for questions to authors`\n\n> _\"Confusion regarding span encoding\"_\n* _\"The proposed method assumes span boundaries are given (lines 281–287)\"_.  \nNot exactly! Lines 281-287 state: “We assume prior knowledge of compound segmentation …” That means the compound and its components are known apriori. However, the associations of the components, i.e. spans, are not known apriori. The example illustrated in Figure 2 clarifies this: “sumitra-ānanda-vardhanaḥ lakṣmaṇaḥ ramam anujagāma” (Translation: “Lakṣmaṇa the one who enhanced the delight of Sumitra, folllowed Rama”). We know the compound word ‘sumitra-ānanda-vardhanaḥ’ and its constituents apriori. There are 3 possible spans: (a) sumitra-ānanda (b)  ānanda-vardhanaḥ (c) sumitra-ānanda-vardhanaḥ. As per our formulation, the correct solution is <<sumitra-<ānanda-vardhanaḥ>{Endocentric}>{Endocentric}. The spans are shown with brackets and corresponding semantic labels are shown with curly brackets in the example. The task is to find the compatible subset of all possible spans and their semantic labels. (a) is not a correct span in this example.\n\n* _\"however, the text in line 427 incorrectly suggests the system ‘predicts’ the spans\"_.   \nIt is absolutely correct. As per our problem formulation, the proposed task is identifying spans and their associated labels. Therefore, the system must predict the spans and associated labels.\n\n* _\"Also, text in line 482—485 suggest accurate identification of spans.\"_.  \n True! Clarified in the previous response.\n\n* _\"If I understand correctly, the model assumes the gold span information is already available in the input (one-hot span encoding). If so, it's an unfair comparison in Figure 8.\"_.    \nNot quite accurate! Figure 8 is fairly compared, as all baselines are informed with the information of the global span. We had clarified this in lines 365-367: “We adapt these systems (baselines) to the NeCTIS task by providing the location of the compounds to ensure a fair comparison with our proposed system.” To provide more insights into span identification, we are reporting F1-Score for unlabeled span identification against the number of components on NeCTIS with context coarse level setting. We will update these numbers in our final draft version.\n\n**F1-Score for unlabeled span identification against the number of components.**\n\n\n|  Model  |    2   |   3   |   4   |   5   |   6   |   7   |   8   |   9   |   10  | Overall |\n|:-------:|:------:|:-----:|:-----:|:-----:|:-----:|:-----:|:-----:|:-----:|:-----:|:-------:|\n|  BoTCP  |  99.71 |  0.04 |  9.76 |  1.63 |  1.05 |  3.13 |  0.00 |  0.00 | 46.10 |  76.18  |\n|    CP   |  99.17 | 19.16 | 13.96 |  6.12 |  8.42 |  7.81 |  7.14 |  4.35 | 30.77 |  61.67  |\n|  LexCP  |  95.63 | 77.35 | 60.06 | 57.55 | 36.84 | 32.81 | 32.14 | 30.44 | 61.54 |  93.33  |\n| Seq2Seq |  96.09 | 74.68 | 54.05 | 54.29 | 37.90 | 28.13 | 28.57 | **39.13** | 53.85 |  90.29  |\n|  SpanCL |  98.75 | 72.49 | 57.06 | 51.43 | 35.79 | 34.38 | 32.14 | 34.78 | 46.15 |  93.06  |\n|   Ours  | **100.00** | **88.61** | **74.17** | **61.63** | **48.98** | **35.39** | **42.86** | 31.82 | **61.54** |  **94.88**  |\n\n> _\"Lines 442—445 incorrectly suggest baselines don’t gain from the context. It should be rephrased to indicate BotCP benefits from the context.\"_\n\nSure. As per your suggestion, we will update the final draft to add more clarity: “While most baselines (except BotCP) do not benefit from contextual information ...”\n\n> _\"Is the dataset transliterated? If not, what models are used for word embeddings?\"_\n\nYes! We transliterate the dataset to meet the needs of respective baselines. We use FastText word embeddings. Lines 29-31: “The codebases of our framework, benchmarked baselines, and the newly annotated datasets are shared with the supplementary material.” We will elaborate it on the availability of additional space.\n\n> _\"Evaluation metrics\"_\n*  _\"What are the standard metrics for binary compound identification task?\"_   \nThe binary compound identification task is a classification task, where standard metrics are P, R, F1, and EM on predicted labels.\n* _\"Are they P, R, F1, and EM on span tuples?\"_   \nYes! P, R, F1, and EM on span tuples. Here, the proposed task is a word-level structured prediction task, where the task is to identify spans and their semantic labels. Please note the key difference between applying these metrics on predicted labels (classification task) and span tuples (structured prediction task).\n* _\"It would be useful to provide references to prior work.\"_.  \nFor the binary compound identification task, the evaluation metrics consisted of P, R, F1, and EM applied to predicted labels. In our proposed task, we assess these same metrics—P, R, F1, and EM—applied to span tuples, which clearly indicates a difference in interpretation. Therefore, we refrained from referring to the evaluation settings of the prior binary compound identification works. We will clarify this difference in our final draft version.\n* _\"If the dependency formulation is the most appropriate, why not use standard dependency parsing metrics such as unlabeled and labeled attachment scores.\"_.  \n    - The question raised is intriguing. In Section 2, our Problem Formulation defines task outputs as tuples containing spans and semantic labels, making it obvious and straightforward to apply P, R, F1, and EM metrics to these span tuples.   \n    - In other words, the reported numbers in our paper belongs to labeled span identification metric in terms of P, R, F1, and EM metrics.   \n    - The dependency formulation is our novelty to solve the task effectively and efficiently. However, the proposed task is not dependency parsing.   \n    - If we use the UAS/LAS metric, then that may lead to various intricacies: (a) Our focus is the identification of multi-component compounds, yet the formulation includes additional labels to accommodate non-compound words. The rationale for incorporating these in the evaluation metric is not evident.  (b) Comparing with baseline results necessitates converting their P, R, F1, and EM metrics to UAS/LAS, a non-trivial conversion. (c) UAS/LAS might not suit future comparisons with diverse formulations. \n    - Considering your suggestion, we will also report unlabeled span identification metric in terms of P, R, F1, and EM metrics in final draft version.\n\n> _\"Include examples of coarse and fine-grained types.\"_. \n\nSure. There are 4 broad semantic types of compounds: Avyayībhava (Indeclinable), Bahuvrīhi (Exocentric), Tatpuruṣa (Endocentric) and Dvandva (Copulative). Again, each broader class is divided into multiple subclasss, leading to 86 fine-grained types. We will update the list of fine-grained labels and the examples in our final draft in the Appendix.\n\n> _\"Any thoughts on why the context doesn’t help the proposed method?\"_.  \n    \nWe had clarified this in lines 492-498: “As the number of components grows, the number of potential solutions increases exponentially, leading to poor performance by the systems in such scenarios. Due to this exponential possibility, contextual information provides limited improvements compared to binary compound identification settings (Sandhan et al., 2022a)” In other words, unlike the context-free setting, the introduction of context information does not warrant an expectation for the system to precisely generate the correct solution from the exponential candidate space. Figure 3 provides a visual representation that elucidates the concept of this exponential candidate space.\n\n> _\"What are some qualitative differences in compounds in prose vs poetry text?\"_.   \n\nPoetry commonly uses multi-component compounding extensively (more exocentric compounds) to adhere to metrical constraints and convey complex concepts. Conversely, prose uses compounds in a more direct and less condensed manner. Furthermore, poets in the realm of poetry often enjoy the freedom to form novel compounds or employ unconventional ones to conform to meter requirements, rendering these compounds infrequent in regular usage.\n\n> _\"What guidelines were provided to the annotators? Consider including them in the appendix.\"_.  \n\nSure, we will update the appendix with this information. Annotators were provided comprehensive guidelines including (1) Details of coarse and fine-grained labels (2) illustrative examples of each tag (3) how to perform compound segmentation (4) how to tag nested information along with its tag  (5) clarification on frequently asked questions on Sanskrit compounding. (It is a 20 page long document.)\n\n> _\"Diverse pool of annotators actually highlights the need for measuring inter-annotator agreement. While I understand many of them could be experts, IAA can vary based on the provided annotation guidelines.\"_. \n\nWe could not elaborate much on it due to space constraints. We had a sufficient annotation budget to employ 6 institutes, each consisting of approximately 10 team members (Lines 342-343). In each team, the diverse pool set was used in a hierarchical manner. There were 3 levels in the hierarchy: Junior linguist (Masters degree in Sanskrit), Senior linguist (Ph.D. in Sanskrit) and professional linguist (Professor in Sanskrit). The annotations from lower expertise were further checked as per the above-mentioned hierarchy. Line 346-351: “Subsequently, the annotated data underwent an exchange process with another team for correctness verification. Any ambiguities encountered during the annotation process were resolved through collective discussions conducted by the correctness-checking team.” \n\nThe annotation guidelines are essentially based on Sanskrit grammar which provides the syntactic and semantic criteria for annotation. Elaborate commentaries accompany the majority of the texts, that discuss the semantics associated with the compounds, which are typically studied by students as a part of their coursework.  Given these considerations, it is very unlikely for professional linguists, often professors instructing these texts, to make mistakes.The dataset was curated around 12 years ago, primarily with the aim of producing error-free gold-standard data. Consequently, the errors made by junior annotators were not recorded or measured, aligning with our focus on achieving error-free quality.", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "1. The task is well-motivated and the author(s) present a detailed comparison against nested NER and MWE detection. The paper mentions >41% of compounds in the literature contain more than three components, and prior work limited themselves to binary compounds.\n2. The dataset is well curated and could be useful to future work on this task. The inclusion of an out-of-domain split presents an interesting challenge to systems.\n3. The paper does a great job at comparing various formulations of this task, including constituency parsing, span labeling and seq2seq.", "reasons_to_reject": "1. The paper can benefit from additional details on dataset collection, and baseline description (see questions below).\n2. The formulation of the task as a dependency parsing problem is not convincing to me. In Figure 4, there are many dependency relations (global relation) and additional tokens (global token) that don't seem to directly help solve the task. Given that the context is not helpful empirically, can we consider a formulation where only the tokens in the compounds constitute the input? This reduces the label space and makes the system more efficient.", "questions_to_authors": "1. Confusion regarding span encoding,\n    - The proposed method assumes span boundaries are given (lines 281–287)\n    - however, the text in line 427 incorrectly suggests the system ‘predicts’ the spans\n    - also, text in line 482—485 suggest accurate identification of spans.\n    - If I understand correctly, the model assumes the gold span information is already available in the input (one-hot span encoding). If so, it's an unfair comparison in Figure 8.\n2. Lines 442—445 incorrectly suggest baselines don’t gain from the context. It should be rephrased to indicate BotCP benefits from the context.\n3. What guidelines were provided to the annotators? Consider including them in the appendix.\n4. Diverse pool of annotators actually highlights the need for measuring inter-annotator agreement. While I understand many of them could be experts, IAA can vary based on the provided annotation guidelines.\n5. Is the dataset transliterated? If not, what models are used for word embeddings?\n6. Evaluation metrics\n    - What are the standard metrics for binary compound identification task? Are they P, R, F1, and EM on span tuples? It would be useful to provide references to prior work.\n    - If the dependency formulation is the most appropriate, why not use standard dependency parsing metrics such as unlabeled and labeled attachment scores.\n7. Include examples of coarse and fine-grained types.\n8. Any thoughts on why the context doesn’t help the proposed method?\n9. What are some qualitative differences in compounds in prose vs poetry text?", "paper_topic_and_main_contributions": "This paper studies multi-component (up to 10) compound-type identification in Sanskrit. They build upon prior work on binary compounds and present a new dataset and benchmark competitive systems for this task.\n\nThe dataset is curated by a large group of language experts and constitutes coarse (4) and fine-grained types (86). The dataset also includes an out-of-domain test set with compounds collected from poetry literature.\n\nThe proposed method formulates the compound-type identification task as a variant of dependency parsing, with special labels for non-compound tokens. This method outperforms baselines that formulate the task as constituency parsing, span labeling, and seq2seq.", "excitement": 4, "reproducibility": 4, "ethical_concerns": "No", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {}, "extra_text": {}}]

Author rebuttal:

Meta-review: All reviewers appreciate the work that went into creating the dataset and see the importance of studying complex compounds. However, all reviewers would also welcome clearer explanations, especially of the dataset and annotation procedure, which the authors replied to positively in their rebuttal. All reviewers acknowledged the rebuttals and one reviewer even went into lengthy back-and-forth discussions and increased their scores. Average soundness is at 3.3, and excitement at 3, where the reviewer with the lowest confidence scores providing the lowest scores.
One reviewer mentions that the method could be applied to other languages. It is very nice to see a contribution on a less-studied language such as Sanskrit, but other languages could indeed benefit from such analyses as well. The dataset pointed to does not exactly correspond to the authors needs, but perhaps the authors would like to check the links provided at the bottom that are relevant for bracketing English noun compounds, and the accompanying data (just structure, not labeled relations).

https://aclanthology.org/R15-1094.pdf, https://aclanthology.org/W15-0112/, and David Vadas. 2009. Statistical Parsing of Noun Phrase Structure. Ph.D. thesis.
Decision: Accept-Findings