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0a55859a36d0887ba4febc98762715_4 | In this paper, we propose a new encoder, by improving GLAD architecture<cite> (Zhong et al., 2018)</cite> . | uses |
0a55859a36d0887ba4febc98762715_5 | First, section 2.1 explains the recently proposed GLAD encoder<cite> (Zhong et al., 2018)</cite> architecture, followed by our proposed encoder in section 2.2. | uses background |
0a55859a36d0887ba4febc98762715_6 | Here, we employ the similar approach of learning slot-specific temporal and context representation of user utterance and system actions, as proposed in GLAD<cite> (Zhong et al., 2018)</cite> . | uses |
0a55859a36d0887ba4febc98762715_7 | Scoring Model: We follow the proposed architecture in GLAD<cite> (Zhong et al., 2018)</cite> for computing score of each slot-value pair, in the user utterance and previous system actions. | uses |
0a55859a36d0887ba4febc98762715_8 | The joint goal is the accumulation of turn goals as described in <cite>Zhong et al. (2018)</cite> . | uses similarities |
0a55859a36d0887ba4febc98762715_9 | The evaluation metric is based on joint goal and turn-level request and joint goal tracking accuracy. The joint goal is the accumulation of turn goals as described in <cite>Zhong et al. (2018)</cite> . | uses |
0a93feafef3ba2d4bb5360ff215171_0 | Several metrics have been proposed recently for evaluating VQA systems (see section 2), but accuracy is still the most commonly used evaluation criterion [4, 11, 23, 42, 44, <cite>1</cite>, 5, 14, 45, 2] . | background |
0a93feafef3ba2d4bb5360ff215171_1 | In recent years, a number of VQA datasets have been proposed: VQA 1.0 [4] , VQA-abstract [<cite>1</cite>] , VQA 2.0 [47, 14] , FM-IQA [13] , DAQUAR [24] , COCO-QA [30] , Visual Madlibs [46] , Visual Genome [20] , VizWiz [16] , Visual7W [48] , TDIUC [18] , CLEVR [17] , SHAPES [3] , Visual Reasoning [34] , Embodied QA [7] . What all these resources have in common is the task for which they were designed: Given an image (either real or abstract) and a question in natural language, models are asked to correctly answer the question. | background |
0a93feafef3ba2d4bb5360ff215171_2 | Being simple to compute and interpret, this metric (hence, VQA3+) is the standard evaluation criterion for open-ended VQA [4, <cite>1</cite>, 16, 47, 14] . | background |
0a93feafef3ba2d4bb5360ff215171_3 | Moreover, it only works with rigid semantic concepts, making it not suitable for phrasal or sentence answers that can be found in [4, <cite>1</cite>, 16, 47, 14] . | background |
0a93feafef3ba2d4bb5360ff215171_4 | This is crucial since, as shown in Figure 3 , in current datasets the proportion of samples with a perfect inter-annotator agreement (i.e., 1 unique answer) is relatively low: 35% in VQA 1.0 [4] , 33% in VQA 2.0 [14] , 43% in VQA-abstract [<cite>1</cite>] , and only 3% in VizWiz [16] . | background |
0a93feafef3ba2d4bb5360ff215171_5 | We tested the validity of our metric by experimenting with four VQA datasets: VQA 1.0 [4] , VQA 2.0 [14] , VQA-abstract [<cite>1</cite>] , and VizWiz [16] . | uses |
0a93feafef3ba2d4bb5360ff215171_6 | To enable a fair comparison across the datasets, for each dataset we followed the same pipeline: The standard VQA model used in [<cite>1</cite>] was trained on the training split and tested on the validation split. | uses |
0ae49d1618e18eb794666543d924ed_0 | By adding very simple CLM-based features to the system, our scores approach those of a state-of-the-art NER system<cite> (Lample et al., 2016)</cite> across multiple languages, demonstrating both the unique importance and the broad utility of this approach. | similarities |
0ae49d1618e18eb794666543d924ed_1 | 4 We compare the CLM's Entity Identification against two state-of-the-art NER systems: CogCompNER (Khashabi et al., 2018) and LSTM-CRF<cite> (Lample et al., 2016)</cite> . | uses |
0ae49d1618e18eb794666543d924ed_2 | 4 We compare the CLM's Entity Identification against two state-of-the-art NER systems: CogCompNER (Khashabi et al., 2018) and LSTM-CRF<cite> (Lample et al., 2016)</cite> . As Table 2 shows, the result of Ngram CLM, which yields the highest performance, is remarkably close to the result of state-of-theart NER systems (especially for English) given the simplicity of the model. | similarities |
0ae49d1618e18eb794666543d924ed_3 | CogCompNER is run with standard features, including Brown clusters;<cite> (Lample et al., 2016)</cite> is run with default parameters and pre-trained embeddings. | uses |
0ae49d1618e18eb794666543d924ed_4 | We compare with the state-of-theart character-level neural NER system of<cite> (Lample et al., 2016)</cite> , which inherently encodes comparable information to CLMs, as a way to investigate how much of that system's performance can be attributed directly to name-internal structure. | uses |
0ae49d1618e18eb794666543d924ed_5 | The results in Table 3 show that for six of the eight languages we studied, the baseline NER can be significantly improved by adding simple CLM features; for English and Arabic, it performs better even than the neural NER model of<cite> (Lample et al., 2016)</cite> . | differences |
0ae49d1618e18eb794666543d924ed_6 | While the end-to-end model developed by<cite> (Lample et al., 2016)</cite> clearly includes information comparable to that in the CLM, it requires a fully annotated NER corpus, takes significant time and computational resources to train, and is non-trivial to integrate into a new NER system. | motivation |
0ae49d1618e18eb794666543d924ed_7 | While the end-to-end model developed by<cite> (Lample et al., 2016)</cite> clearly includes information comparable to that in the CLM, it requires a fully annotated NER corpus, takes significant time and computational resources to train, and is non-trivial to integrate into a new NER system. The CLM approach captures a very large fraction of the entity/non-entity distinction capacity of full NER systems, and can be rapidly trained using only entity and non-entity token lists -i.e., it is corpus-agnostic. | motivation differences |
0ae49d1618e18eb794666543d924ed_8 | <cite>Lample et al. (2016)</cite> use character embeddings in an LSTM-CRF model. | background |
0af8cacc0f85bb557e1943e32450e2_0 | We present a replication study of BERT pretraining<cite> (Devlin et al., 2019)</cite> that carefully measures the impact of many key hyperparameters and training data size. | uses |
0af8cacc0f85bb557e1943e32450e2_1 | Self-training methods such as ELMo (Peters et al., 2018) , GPT (Radford et al., 2018) , BERT<cite> (Devlin et al., 2019)</cite> , XLM (Lample and Conneau, 2019) , and XLNet have brought significant performance gains, but it can be challenging to determine which aspects of the methods contribute the most. | motivation background |
0af8cacc0f85bb557e1943e32450e2_2 | We present a replication study of BERT pretraining<cite> (Devlin et al., 2019)</cite> , which includes a careful evaluation of the effects of hyperparmeter tuning and training set size. | uses |
0af8cacc0f85bb557e1943e32450e2_3 | We present a replication study of BERT pretraining<cite> (Devlin et al., 2019)</cite> , which includes a careful evaluation of the effects of hyperparmeter tuning and training set size. We find that BERT was significantly undertrained and propose an improved recipe for training BERT models, which we call RoBERTa, that can match or exceed the performance of all of the post-BERT methods. | extends |
0af8cacc0f85bb557e1943e32450e2_4 | In this section, we give a brief overview of the BERT<cite> (Devlin et al., 2019)</cite> pretraining approach and some of the training choices that we will examine experimentally in the following section. | uses background |
0af8cacc0f85bb557e1943e32450e2_5 | Unlike<cite> Devlin et al. (2019)</cite> , we do not randomly inject short sequences, and we do not train with a reduced sequence length for the first 90% of updates. | differences |
0af8cacc0f85bb557e1943e32450e2_6 | Our finetuning procedure follows the original BERT paper<cite> (Devlin et al., 2019)</cite> . | uses |
0af8cacc0f85bb557e1943e32450e2_7 | For SQuAD V1.1 we adopt the same span prediction method as BERT<cite> (Devlin et al., 2019)</cite> . | uses |
0af8cacc0f85bb557e1943e32450e2_8 | Results Table 1 compares the published BERT BASE results from<cite> Devlin et al. (2019)</cite> to our reimplementation with either static or dynamic masking. | uses |
0af8cacc0f85bb557e1943e32450e2_9 | Results Table 1 compares the published BERT BASE results from<cite> Devlin et al. (2019)</cite> to our reimplementation with either static or dynamic masking. We find that our reimplementation with static masking performs similar to the original BERT model, and dynamic masking is comparable or slightly better than static masking. | uses similarities |
0af8cacc0f85bb557e1943e32450e2_10 | • SEGMENT-PAIR+NSP: This follows the original input format used in BERT<cite> (Devlin et al., 2019)</cite> , with the NSP loss. | uses |
0af8cacc0f85bb557e1943e32450e2_11 | We first compare the original SEGMENT-PAIR input format from<cite> Devlin et al. (2019)</cite> to the SENTENCE-PAIR format; both formats retain the NSP loss, but the latter uses single sentences. | uses |
0af8cacc0f85bb557e1943e32450e2_12 | We find that this setting outperforms the originally published BERT BASE results and that removing the NSP loss matches or slightly improves downstream task performance, in contrast to<cite> Devlin et al. (2019)</cite> . | differences |
0af8cacc0f85bb557e1943e32450e2_13 | The original BERT implementation<cite> (Devlin et al., 2019)</cite> uses a character-level BPE vocabulary of size 30K, which is learned after preprocessing the input with heuristic tokenization rules. | background |
0af8cacc0f85bb557e1943e32450e2_14 | The original BERT implementation<cite> (Devlin et al., 2019)</cite> uses a character-level BPE vocabulary of size 30K, which is learned after preprocessing the input with heuristic tokenization rules. Following Radford et al. (2019) , we instead consider training BERT with a larger byte-level BPE vocabulary containing 50K subword units, without any additional preprocessing or tokenization of the input. | background differences |
0af8cacc0f85bb557e1943e32450e2_15 | For example, the recently proposed XLNet architecture ) is pretrained using nearly 10 times more data than the original BERT<cite> (Devlin et al., 2019)</cite> . | differences |
0af8cacc0f85bb557e1943e32450e2_16 | We pretrain for 100K steps over a comparable BOOK-CORPUS plus WIKIPEDIA dataset as was used in<cite> Devlin et al. (2019)</cite> . | similarities |
0af8cacc0f85bb557e1943e32450e2_17 | This formulation significantly simplifies the task, but is not directly comparable to BERT<cite> (Devlin et al., 2019)</cite> . | differences |
0af8cacc0f85bb557e1943e32450e2_18 | In particular, while both BERT<cite> (Devlin et al., 2019)</cite> and XLNet augment their training data with additional QA datasets, we only finetune RoBERTa using the provided SQuAD training data. | differences |
0af8cacc0f85bb557e1943e32450e2_19 | For SQuAD v1.1 we follow the same finetuning procedure as<cite> Devlin et al. (2019)</cite> . | uses |
0af8cacc0f85bb557e1943e32450e2_20 | Most of the top systems build upon either BERT<cite> (Devlin et al., 2019)</cite> or XLNet , both of which rely on additional external training data. | background |
0af8cacc0f85bb557e1943e32450e2_21 | Most of the top systems build upon either BERT<cite> (Devlin et al., 2019)</cite> or XLNet , both of which rely on additional external training data. In contrast, our submission does not use any additional data. | differences |
0af8cacc0f85bb557e1943e32450e2_22 | Pretraining methods have been designed with different training objectives, including language modeling (Dai and Le, 2015; Peters et al., 2018; Howard and Ruder, 2018) , machine translation (McCann et al., 2017) , and masked language modeling <cite>(Devlin et al., 2019</cite>; Lample and Conneau, 2019) . | background |
0af8cacc0f85bb557e1943e32450e2_23 | Performance is also typically improved by training bigger models on more data <cite>(Devlin et al., 2019</cite>; Yang et al., 2019; Radford et al., 2019) . | background |
0b2e3651610aba4bd7150eee50797f_0 | These approaches were either complicated (Ma et al., 2007; Chang et al., 2008; Ma and Way, 2009; Paul et al., 2010) , or of high computational complexity (Chung and Gildea 2009;<cite> Duan et al., 2010)</cite> . | background |
0b2e3651610aba4bd7150eee50797f_1 | However, this kind of errors cannot be fixed by methods which learn new words by packing already segmented words, such as word packing (Ma et al., 2007) and Pseudo-word <cite>(Duan et al., 2010)</cite> . | background |
0b2e3651610aba4bd7150eee50797f_2 | In this setting, we gradually set the phrase length and the distortion limits of the phrase-based decoder (context size) to 7, 9, 11 and 13, in order to remove the disadvantage of shorter context size of using character as WSR for fair comparison with WordSys as suggested by<cite> Duan et al. (2010)</cite> . | uses |
0b334057bc358f5537497ed15344c1_1 | This is probably the reason for growing interest in creation of annotated corpora [4] , development of methods for augmenting the existing annotation [5] , speeding up the annotation process [5] , and reducing its cost; evaluating the comparability of results obtained applying the same methods to different collections<cite> [6]</cite> , And increasing compatibility of different annotations [7] . | background |
0b334057bc358f5537497ed15344c1_2 | Increasingly sophisticated relation extraction methods <cite>[6,</cite> 8] are being applied to a broader set of iii relations [9] . | background |
0c233d68fb2ccdf033fc6a08c8f4bf_0 | The goal of the <cite>Penn Discourse Treebank (PDTB)</cite> project is to develop a large-scale corpus, annotated with coherence relations marked by discourse connectives. Currently, the primary application of the <cite>PDTB</cite> annotation has been to news articles. | background |
0c233d68fb2ccdf033fc6a08c8f4bf_1 | In this study, we tested whether the <cite>PDTB</cite> guidelines can be adapted to a different genre. | motivation |
0c233d68fb2ccdf033fc6a08c8f4bf_2 | In this study, we tested whether the <cite>PDTB</cite> guidelines can be adapted to a different genre. We annotated discourse connectives and <cite>their</cite> arguments in one 4,937-token full-text biomedical article. | uses |
0c233d68fb2ccdf033fc6a08c8f4bf_3 | Thus our experiments suggest that the <cite>PDTB</cite> annotation can be adapted to new domains by minimally adjusting the guidelines and by adding some further domain-specific linguistic cues. | extends |
0c233d68fb2ccdf033fc6a08c8f4bf_4 | The <cite>Penn Discourse Treebank (PDTB)</cite> (http://www.seas.upenn.edu/~pdtb) (<cite>Prasad et al. 2008a</cite>) annotates the argument structure, semantics, and attribution of discourse connectives and their arguments. | background |
0c233d68fb2ccdf033fc6a08c8f4bf_5 | This work examines whether the <cite>PDTB</cite> annotation guidelines can be adapted to a different genre, the biomedical literature. | motivation |
0c233d68fb2ccdf033fc6a08c8f4bf_6 | Following the <cite>PDTB</cite> annotation manual (Prasad et al. 2008b ), we conducted a pilot annotation of discourse connectivity in biomedical text. | uses |
0c233d68fb2ccdf033fc6a08c8f4bf_7 | When the annotation work was completed, we measured the inter-annotator agreement, following the <cite>PDTB</cite> exact match criterion (Miltsakaki et al. 2004 ). | uses |
0c233d68fb2ccdf033fc6a08c8f4bf_8 | We discussed the annotation results and made suggestions to adapt the <cite>PDTB</cite> guidelines to biomedical text. | extends |
0c233d68fb2ccdf033fc6a08c8f4bf_9 | The <cite>PDTB</cite> also reported a higher level of agreement in annotating Arg2 than in annotating Arg1 (Miltsakaki et al. 2004) . | background |
0c233d68fb2ccdf033fc6a08c8f4bf_10 | The overall agreement for the 68 discourse relations is 45.6% for exact match, 45.6% for Arg1, and 79.4% for Arg2. The <cite>PDTB</cite> also reported a higher level of agreement in annotating Arg2 than in annotating Arg1 (Miltsakaki et al. 2004) . We manually analyzed the cases with disagreement. | differences |
0c233d68fb2ccdf033fc6a08c8f4bf_11 | After the completion of the pilot annotation and the discussion, we decided to add the following conventions to the <cite>PDTB</cite> annotation guidelines to address the characteristics of biomedical text: i. Citation references are to be annotated as a part of an argument because the inclusion will benefit many text-mining tasks including identifying the semantic relations among citations. | extends |
0c233d68fb2ccdf033fc6a08c8f4bf_12 | We will annotate a wider variety of nominalizations as arguments than allowed by the <cite>PDTB</cite> guidelines. | extends |
0c3f9588b6f587d04c286384ca24e0_0 | In this paper we aim to improve the state-of-the-art for the task of learning a TAG supertagger from an annotated treebank <cite>(Kasai et al., 2018)</cite> . | uses |
0c3f9588b6f587d04c286384ca24e0_1 | Our experimental results show that our novel multi-task learning framework leads to a new state-of-the-art accuracy score of 91.39% for TAG supertagging on the Penn Treebank dataset (Marcus et al., 1993; Chen et al., 2006) which is a significant improvement over the previous multi-task result for supertagging that combines supertagging with graph-based parsing <cite>(Kasai et al., 2018)</cite> . | differences |
0c3f9588b6f587d04c286384ca24e0_2 | Neural linear-time transition based parsers are still not accurate enough to compete with the state-of-the-art supertagging models or parsers that use supertagging as the initial step (Chung et al., 2016;<cite> Kasai et al., 2018)</cite> . | background |
0c3f9588b6f587d04c286384ca24e0_3 | For our baseline supertagging model we use the state-of-the-art model that currently has the highest accuracy on the Penn treebank dataset <cite>(Kasai et al., 2018)</cite> . For the supertagging model the main contribution of<cite> Kasai et al. (2018)</cite> was two-fold: the first was to add a character CNN for modeling word embeddings using subword features, and the second was to add highway connections to add more layers to a standard bidirectional LSTM. | uses |
0c3f9588b6f587d04c286384ca24e0_4 | Another extension to the standard sequence prediction model in<cite> Kasai et al. (2018)</cite> was to combine supertagging with graph-based parsing. | extends |
0c3f9588b6f587d04c286384ca24e0_5 | <cite>(Kasai et al., 2018)</cite> we use two components in the word embedding: • a 30-dimensional character level embedding vector computed using a char-CNN which captures the morphological information (Santos and Zadrozny, 2014; Chiu and Nichols, 2016; Ma and Hovy, 2016;<cite> Kasai et al., 2018)</cite> . | uses |
0c3f9588b6f587d04c286384ca24e0_6 | Unlike <cite>(Kasai et al., 2018)</cite> we do not use predicted part of speech (POS) tags as part of the input sequence. | differences |
0c3f9588b6f587d04c286384ca24e0_7 | For the hyperparameters, we use the settings in<cite> Kasai et al. (2018)</cite> in order to ensure a fair comparison. | uses |
0c3f9588b6f587d04c286384ca24e0_8 | Unlike <cite>(Kasai et al., 2018)</cite> we do not use highway connections in our model. | differences |
0c3f9588b6f587d04c286384ca24e0_9 | In our case, because we re-use the same training set for multi-task learning, we have made sure our experimental settings exactly match the previous best state-of-the-art method for supertagging <cite>(Kasai et al., 2018)</cite> and we use the same pre-trained word embeddings to ensure a fair comparison. | uses |
0c3f9588b6f587d04c286384ca24e0_10 | We use the dataset that has been widely used by previous work in supertagging and TAG parsing (Bangalore et al., 2009; Chung et al., 2016; Friedman et al., 2017;<cite> Kasai et al., , 2018</cite> . | uses |
0c3f9588b6f587d04c286384ca24e0_12 | All of those words are<cite> Kasai et al. (2018)</cite> refers to highway connections, and POS refers to the use of predicted part-of-speech tags as inputs. We do not use HW or POS in our models as they do not provide any benefit. | differences |
0c3f9588b6f587d04c286384ca24e0_13 | Neural network based supertagging models in TAG <cite>(Kasai et al., 2018)</cite> and CCG (Xu Lewis et al., 2016; Xu, 2016; Vaswani et al., 2016) have shown substantial improvement in performance, but the supertagging models are all quite similar as they all use a bi-directional RNN feeding into a prediction layer. | background |
0c3f9588b6f587d04c286384ca24e0_14 | <cite>(Kasai et al., 2018)</cite> combines supertagging with parsing which does provide state-of-the-art accuracy but at the expense of computational complexity. | background |
0c3f9588b6f587d04c286384ca24e0_15 | extends the BiLSTM model with predicted part-of-speech tags and suffix embeddings as inputs, then<cite> Kasai et al. (2018)</cite> further extends the BiLSTM model with highway connection as well as character CNN as input, and jointly train the supertagging model with parsing model and this work had the state-of-the-art accuracy before our paper on the Penn treebank dataset. | background |
0cc576e90c5ee2af043e09234792f5_0 | Finally, it would be interesting to determine whether using ASs extracted from a corpus of native texts enables a better prediction than that obtained by using the simple frequency of the unigrams and bigrams<cite> (Yannakoudakis et al., 2011)</cite> . | future_work |
0cc576e90c5ee2af043e09234792f5_1 | Dataset: The analyses were conducted on the First Certificate in English (FCE) ESOL examination scripts described in <cite>Yannakoudakis et al. (2011</cite> Yannakoudakis et al. ( , 2012 . | similarities uses |
0cc576e90c5ee2af043e09234792f5_2 | As in<cite> Yannakoudakis et al. (2011)</cite> , the 1141 texts from the year 2000 were used for training, while the 97 texts from the year 2001 were used for testing. | similarities |
0cc576e90c5ee2af043e09234792f5_3 | Lexical Features: As a benchmark for comparison, the lexical features that were showed to be good predictors of the quality of the texts in this dataset<cite> (Yannakoudakis et al., 2011)</cite> were chosen. | similarities uses |
0cc576e90c5ee2af043e09234792f5_4 | These features were extracted as described in<cite> Yannakoudakis et al. (2011)</cite> ; the only difference is that they used the RASP tagger and not the CLAWS tagger. | extends differences |
0cc576e90c5ee2af043e09234792f5_5 | Supervised Learning Approach and Evaluation: As in<cite> Yannakoudakis et al. (2011)</cite> , the automated scoring task was treated as a rankpreference learning problem by means of the SVM-Rank package (Joachims, 2006) , which is a much faster version of the SVM-Light package used by<cite> Yannakoudakis et al. (2011)</cite> . | extends differences |
0cc576e90c5ee2af043e09234792f5_6 | Since the quality ratings are distributed on a zero to 40 scale, I chose Pearson's correlation coefficient, also used by<cite> Yannakoudakis et al. (2011)</cite> , as the measure of performance. | similarities uses |
0cc576e90c5ee2af043e09234792f5_7 | To get an idea of how well the collocational and lexical features perform, the correlations in Table 2 can be compared to the average correlation between the Examiners' scores reported by<cite> Yannakoudakis et al. (2011)</cite> , which give an upper bound of 0.80 while the All models with more than three bins obtain a correlation of at least 0.75. | similarities |
0d06c8509ebbdc61985bebcdb26e6c_0 | In a similar work, Mnih et al. <cite>[13]</cite> proposed to use Noise Contrastive Estimation (NCE) [14] to speed-up the training. | background |
0d06c8509ebbdc61985bebcdb26e6c_1 | Hence, an adaptive IS may use a large number of samples to solve this problem whereas NCE is more stable and requires a fixed small number of noise samples (e.g., 100) to achieve a good performance<cite> [13,</cite> 16] . | background |
0d06c8509ebbdc61985bebcdb26e6c_2 | Furthermore, we can show that this solution optimally approximates the sampling from a unigram distribution, which has been shown to be a good noise distribution choice<cite> [13,</cite> 16] . | background |
0d06c8509ebbdc61985bebcdb26e6c_3 | Hence, we solely focus our experiments on NCE as a major approach to achieve this goal [17,<cite> 13,</cite> 16] in comparison to the reference full softmax function. | uses |
0d06c8509ebbdc61985bebcdb26e6c_4 | Following the setup proposed in<cite> [13,</cite> 16] , S-NCE uses K = 100 noise samples, whereas B-NCE uses only the target words in the batch (K=0). | uses |
0d1fb27d847ca44af36862cf78744e_0 | In addition, there are several approaches to non-projective dependency parsing that are still to be evaluated in the large (Covington, 1990;<cite> Kahane et al., 1998</cite>; Duchier and Debusmann, 2001; Holan et al., 2001; Hellwig, 2003) . | background |
0d1fb27d847ca44af36862cf78744e_1 | First, the training data for the parser is projectivized by applying a minimal number of lifting operations<cite> (Kahane et al., 1998)</cite> and encoding information about these lifts in arc labels. | background |
0d1fb27d847ca44af36862cf78744e_3 | As observed by <cite>Kahane et al. (1998)</cite> , any (nonprojective) dependency graph can be transformed into a projective one by a lifting operation, which replaces each non-projective arc w j → w k by a projective arc w i → w k such that w i → * w j holds in the original graph. | background |
0d1fb27d847ca44af36862cf78744e_4 | Using the terminology of <cite>Kahane et al. (1998)</cite> , we say that jedna is the syntactic head of Z, while je is its linear head in the projectivized representation. | uses |
0d1fb27d847ca44af36862cf78744e_5 | Unlike <cite>Kahane et al. (1998)</cite> , we do not regard a projectivized representation as the final target of the parsing process. | differences |
0d798fcdee6ee5722d6dc5638210c2_0 | Recent state-of-the-art models (Wang et al., 2018;<cite> Fried et al., 2018b</cite>; Ma et al., 2019) have demonstrated large gains in accuracy on the VLN task. | background |
0d798fcdee6ee5722d6dc5638210c2_1 | In this paper, we find that agents without any visual input can achieve competitive performance, matching or even outperforming their vision-based counterparts under two state-of-theart model models<cite> (Fried et al., 2018b</cite>; Ma et al., 2019) . | motivation |
0d798fcdee6ee5722d6dc5638210c2_2 | In this paper, we show that the same trends hold for two recent state-of-the-art architectures (Ma et al., 2019;<cite> Fried et al., 2018b)</cite> for the VLN task; we also analyze to what extent object-based representations and mixture-ofexperts methods can address these issues. | similarities |