The Gap of Semantic Parsing: A Survey on Automatic Math Word Problem Solvers <s> Coreference Resolution <s> In recent years there has been substantial work on the important problem of coreference resolution, most of which has concentrated on the development of new models and algorithmic techniques. These works often show that complex models improve over a weak pairwise baseline. However, less attention has been given to the importance of selecting strong features to support learning a coreference model. ::: ::: This paper describes a rather simple pairwise classification model for coreference resolution, developed with a well-designed set of features. We show that this produces a state-of-the-art system that outperforms systems built with complex models. We suggest that our system can be used as a baseline for the development of more complex models -- which may have less impact when a more robust set of features is used. The paper also presents an ablation study and discusses the relative contributions of various features. <s> BIB001 </s> The Gap of Semantic Parsing: A Survey on Automatic Math Word Problem Solvers <s> Coreference Resolution <s> Coreference systems are driven by syntactic, semantic, and discourse constraints. We present a simple approach which completely modularizes these three aspects. In contrast to much current work, which focuses on learning and on the discourse component, our system is deterministic and is driven entirely by syntactic and semantic compatibility as learned from a large, unlabeled corpus. Despite its simplicity and discourse naivete, our system substantially outperforms all unsupervised systems and most supervised ones. Primary contributions include (1) the presentation of a simple-to-reproduce, high-performing baseline and (2) the demonstration that most remaining errors can be attributed to syntactic and semantic factors external to the coreference phenomenon (and perhaps best addressed by non-coreference systems). <s> BIB002 </s> The Gap of Semantic Parsing: A Survey on Automatic Math Word Problem Solvers <s> Coreference Resolution <s> Most coreference resolution models determine if two mentions are coreferent using a single function over a set of constraints or features. This approach can lead to incorrect decisions as lower precision features often overwhelm the smaller number of high precision ones. To overcome this problem, we propose a simple coreference architecture based on a sieve that applies tiers of deterministic coreference models one at a time from highest to lowest precision. Each tier builds on the previous tier's entity cluster output. Further, our model propagates global information by sharing attributes (e.g., gender and number) across mentions in the same cluster. This cautious sieve guarantees that stronger features are given precedence over weaker ones and that each decision is made using all of the information available at the time. The framework is highly modular: new coreference modules can be plugged in without any change to the other modules. In spite of its simplicity, our approach outperforms many state-of-the-art supervised and unsupervised models on several standard corpora. This suggests that sieve-based approaches could be applied to other NLP tasks. <s> BIB003 </s> The Gap of Semantic Parsing: A Survey on Automatic Math Word Problem Solvers <s> Coreference Resolution <s> This paper details the coreference resolution system submitted by Stanford at the CoNLL-2011 shared task. Our system is a collection of deterministic coreference resolution models that incorporate lexical, syntactic, semantic, and discourse information. All these models use global document-level information by sharing mention attributes, such as gender and number, across mentions in the same cluster. We participated in both the open and closed tracks and submitted results using both predicted and gold mentions. Our system was ranked first in both tracks, with a score of 57.8 in the closed track and 58.3 in the open track. <s> BIB004 </s> The Gap of Semantic Parsing: A Survey on Automatic Math Word Problem Solvers <s> Coreference Resolution <s> Many errors in coreference resolution come from semantic mismatches due to inadequate world knowledge. Errors in named-entity linking (NEL), on the other hand, are often caused by superficial modeling of entity context. This paper demonstrates that these two tasks are complementary. We introduce NECO, a new model for named entity linking and coreference resolution, which solves both problems jointly, reducing the errors made on each. NECO extends the Stanford deterministic coreference system by automatically linking mentions to Wikipedia and introducing new NEL-informed mention-merging sieves. Linking improves mention-detection and enables new semantic attributes to be incorporated from Freebase, while coreference provides better context modeling by propagating named-entity links within mention clusters. Experiments show consistent improvements across a number of datasets and experimental conditions, including over 11% reduction in MUC coreference error and nearly 21% reduction in F1 NEL error on ACE 2004 newswire data. <s> BIB005 </s> The Gap of Semantic Parsing: A Survey on Automatic Math Word Problem Solvers <s> Coreference Resolution <s> A. Bjorkelund and R. Farkas. 2012. Data-driven multilingual coreference resolution using resolver stacking. In Joint Conference on EMNLP and CoNLL Shared Task. K.-W. Chang, R. Samdani, A. Rozovskaya, M. Sammons, and D. Roth. 2012. Illinois-coref: The UI system in the CoNLL-2012 Shared Task. In CoNLL Shared Task. C. Chen and V. Ng. 2012. Combining the best of two worlds: A hybrid approach to multilingual coreference resolution. In Joint Conference on EMNLP and CoNLL Shared Task. E. R. Fernandes, C. N. dos Santos, and R. L. Milidiu. 2012. Latent structure perceptron with feature induction for unrestricted coreference resolution. In Joint Conference on EMNLP and CoNLL Shared Task. S. Martschat, J. Cai, S. Broscheit, E. Mujdricza-Maydt, and M. Strube. 2012. A multigraph model for coreference resolution. In Joint Conference on EMNLP and CoNLL Shared Task, July. <s> BIB006 </s> The Gap of Semantic Parsing: A Survey on Automatic Math Word Problem Solvers <s> Coreference Resolution <s> This paper presents a novel approach to learning to solve simple arithmetic word problems. Our system, ARIS, analyzes each of the sentences in the problem statement to identify the relevant variables and their values. ARIS then maps this information into an equation that represents the problem, and enables its (trivial) solution as shown in Figure 1. The paper analyzes the arithmetic-word problems “genre”, identifying seven categories of verbs used in such problems. ARIS learns to categorize verbs with 81.2% accuracy, and is able to solve 77.7% of the problems in a corpus of standard primary school test questions. We report the first learning results on this task without reliance on predefined templates and make our data publicly available. 1 <s> BIB007 </s> The Gap of Semantic Parsing: A Survey on Automatic Math Word Problem Solvers <s> Coreference Resolution <s> Little work from the Natural Language Processing community has targeted the role of quantities in Natural Language Understanding. This paper takes some key steps towards facilitating reasoning about quantities expressed in natural language. We investigate two different tasks of numerical reasoning. First, we consider Quantity Entailment, a new task formulated to understand the role of quantities in general textual inference tasks. Second, we consider the problem of automatically understanding and solving elementary school math word problems. In order to address these quantitative reasoning problems we first develop a computational approach which we show to successfully recognize and normalize textual expressions of quantities. We then use these capabilities to further develop algorithms to assist reasoning in the context of the aforementioned tasks. <s> BIB008 </s> The Gap of Semantic Parsing: A Survey on Automatic Math Word Problem Solvers <s> Coreference Resolution <s> This paper presents a new algorithm to automatically solve algebra word problems. Our algorithm solves a word problem via analyzing a hypothesis space containing all possible equation systems generated by assigning the numbers in the word problem into a set of equation system templates extracted from the training data. To obtain a robust decision surface, we train a log-linear model to make the margin between the correct assignments and the false ones as large as possible. This results in a quadratic programming (QP) problem which can be efficiently solved. Experimental results show that our algorithm achieves 79.7% accuracy, about 10% higher than the state-of-the-art baseline (Kushman et al., 2014). <s> BIB009 </s> The Gap of Semantic Parsing: A Survey on Automatic Math Word Problem Solvers <s> Coreference Resolution <s> Math word problems form a natural abstraction to a range of quantitative reasoning problems, such as understanding financial news, sports results, and casualties of war. Solving such problems requires the understanding of several mathematical concepts such as dimensional analysis, subset relationships, etc. In this paper, we develop declarative rules which govern the translation of natural language description of these concepts to math expressions. We then present a framework for incorporating such declarative knowledge into word problem solving. Our method learns to map arithmetic word problem text to math expressions, by learning to select the relevant declarative knowledge for each operation of the solution expression. This provides a way to handle multiple concepts in the same problem while, at the same time, support interpretability of the answer expression. Our method models the mapping to declarative knowledge as a latent variable, thus removing the need for expensive annotations. Experimental evaluation suggests that our domain knowledge based solver outperforms all other systems, and that it generalizes better in the realistic case where the training data it is exposed to is biased in a different way than the test data. <s> BIB010
Co-reference resolution involves the identification and clustering of noun phrases mentions that refer to the same real-world entity. The MWP solvers use it as a pre-processing step to ensure the correct arithmetic operations or value update on the same entity. BIB002 is an early deterministic approach which is driven entirely by the syntactic and semantic compatibility learned from a large, unlabeled corpus. It allows proper and nominal mentions to only corefer with antecedents that have the same head, but pronominal mentions to corefer with any antecedent. On top of BIB002 , Raghunathan et al. BIB003 proposed an architecture based on tiers of deterministic coreference models. The tiers are processed from the highest to the lowest precision and the entity output of a tier is forwarded to the next tier for further processing. BIB004 is another model that integrates a collection of deterministic coreference resolution models. Targeting at exploring rich feature space, BIB001 proposed a simple classification model for coreference resolution with a well-designed set of features. NECo is proposed in BIB005 and capable of solving both named entity linking and co-reference resolution jointly. As to applying coreference resolvers in MWP sovers, the Illinois Coreference Resolver BIB001 BIB006 is used in BIB008 to identify pronoun referents and facilitate semantic labeling. In BIB010 , a rule function Coref (A, B) , which is true when A and B represent the same entity, is derived as a component of the declarative rules to determine the math operators. Given a pair of sentences, each containing a quantity, ZDC BIB009 takes into account the existence of coreference relationship between these two sentences for feature exploitation. Meanwhile, ARIS BIB007 adopts the BIB003 for coreference resolution and uses the predicted coreference relationships to replace pronouns with their coreferenent links.