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260489da0fb3f7a201a6a1cce8f03b_4 | <cite>Bahdanau et al. [2015]</cite> define the conditional probability in Eq. (1) as where g is a non-linear function and s n is the hidden state corresponding to the n-th target word computed by | background |
260489da0fb3f7a201a6a1cce8f03b_5 | We follow <cite>Bahdanau et al. [2015]</cite> to restrict that sentences are no longer than 50 words. | uses |
260489da0fb3f7a201a6a1cce8f03b_6 | GroundHog is an attention-based neural machine translation system<cite> [Bahdanau et al., 2015]</cite> . | background |
260489da0fb3f7a201a6a1cce8f03b_7 | We compared our approach with two state-of-the-art SMT and NMT systems: [Koehn and Hoang, 2007] . GroundHog is an attention-based neural machine translation system<cite> [Bahdanau et al., 2015]</cite> . 1. Moses [Koehn and Hoang, 2007] : a phrase-based SMT system; 2. GroundHog<cite> [Bahdanau et al., 2015]</cite> : an attention-based NMT system. | uses |
260489da0fb3f7a201a6a1cce8f03b_8 | GroundHog is an attention-based neural machine translation system<cite> [Bahdanau et al., 2015]</cite> . Our approach simply extends GroundHog by replacing independent training with agreement-based joint training. | extends background |
260489da0fb3f7a201a6a1cce8f03b_9 | <cite>Bahdanau et al. [2015]</cite> first introduce the attentional mechanism into neural machine translation to enable the decoder to focus on relevant parts of the source sentence during decoding. | background |
260489da0fb3f7a201a6a1cce8f03b_10 | After analyzing the alignment matrices generated by GroundHog<cite> [Bahdanau et al., 2015]</cite> , we find that modeling the structural divergence of natural languages is so challenging that unidirectional models can only capture part of alignment regularities. This finding inspires us to improve attention-based NMT by combining two unidirectional models. | motivation |
260489da0fb3f7a201a6a1cce8f03b_11 | After analyzing the alignment matrices generated by GroundHog<cite> [Bahdanau et al., 2015]</cite> , we find that modeling the structural divergence of natural languages is so challenging that unidirectional models can only capture part of alignment regularities. | motivation |
2606ecb66287c0199f3aa6d95f6774_0 | The progress in both fields has inspired researchers to build holistic architectures for challenging grounding [14, 15] , natural language generation from image/video [16, 17, 18] , image-to-sentence alignment [19, 20, 21, 22] , and recently presented question-answering problems [23, 24, 25, 26, <cite>27]</cite> . | background |
2606ecb66287c0199f3aa6d95f6774_1 | Third, if our aim is to mimic human response, we have to deal with inherent ambiguities due to human judgement that stem from issues like binding, reference frames, social conventions. For instance <cite>[27]</cite> reports that for a question answering task on real-world images even human answers are inconsistent. | motivation |
2606ecb66287c0199f3aa6d95f6774_2 | We exemplify some of our findings on the <cite>DAQUAR dataset</cite> <cite>[27]</cite> with the aim of demonstrating different challenges that are present in <cite>the dataset</cite>. | uses |
2606ecb66287c0199f3aa6d95f6774_3 | The quality of an answer depends on how ambiguous and latent notions of reference frames and intentions are understood <cite>[27</cite>, 44] . | background |
2606ecb66287c0199f3aa6d95f6774_5 | <cite>DAQUAR</cite> <cite>[27]</cite> is a challenging, large dataset for a question answering task based on real-world images. | background |
2606ecb66287c0199f3aa6d95f6774_6 | <cite>DAQUAR's</cite> language scope is beyond the nouns or tuples that are typical to recognition datasets [51, 52, 53] . | background |
2606ecb66287c0199f3aa6d95f6774_7 | In this section we discuss in isolation different challenges reflected in <cite>DAQUAR</cite>. | motivation |
2606ecb66287c0199f3aa6d95f6774_8 | The machine world in <cite>DAQUAR</cite> is represented as a set of images and questions about their content. | background |
2606ecb66287c0199f3aa6d95f6774_9 | <cite>DAQUAR</cite> contains 1088 different nouns in the question, 803 in the answers, and 1586 altogether (we use the Stanford POS Tagger [59] to extract the nouns from the questions). | background |
2606ecb66287c0199f3aa6d95f6774_10 | <cite>DAQUAR</cite> also contains other parts of speech where only colors and spatial prepositions are grounded in <cite>[27]</cite> . | background |
2606ecb66287c0199f3aa6d95f6774_11 | Moreover, ambiguities naturally emerge due to fine grained categories that exist in <cite>DAQUAR</cite>. | background |
2606ecb66287c0199f3aa6d95f6774_12 | <cite>DAQUAR</cite> includes various challenges related to natural language understanding. | motivation |
2606ecb66287c0199f3aa6d95f6774_13 | Common sense knowledge <cite>DAQUAR</cite> includes questions that can be reliably answered using common sense knowledge. | background |
2606ecb66287c0199f3aa6d95f6774_14 | To sum up, we believe that common sense knowledge is an interesting venue to explore with <cite>DAQUAR</cite>. | motivation |
2606ecb66287c0199f3aa6d95f6774_15 | Some authors [23, 24, <cite>27]</cite> treat the grounding (understood here as the logical representation of the meaning of the question) as a latent variable in the question answering task. | background |
2606ecb66287c0199f3aa6d95f6774_16 | We exemplify the aforementioned requirements by illustrating the WUPS scorean automatic metric that quantifies performance of the holistic architectures proposed by <cite>[27]</cite> . | uses |
2606ecb66287c0199f3aa6d95f6774_17 | The authors of <cite>[27]</cite> have proposed using WUP similarity [62] as the membership measure 碌 in the WUPS score. Such choice of 碌 suffers from the aforementioned coverage problem and the whole metric takes only one human interpretation of the question into account. Future directions for defining metrics Recent work provides several directions towards improving scores. | future_work |
2606ecb66287c0199f3aa6d95f6774_18 | We identify particular challenges that holistic tasks should exhibit and exemplify how they are manifested in <cite>a recent question answering challenge</cite> <cite>[27]</cite> . To judge competing architectures and measure the progress on the task, we suggest several directions to further improve existing metrics, and discuss different experimental scenarios. | future_work |
264bdb348c13f167768fd859b047e8_0 | <cite>Olabiyi et al. [7]</cite> tackle this problem by training a modified HRED generator alongside an adversarial discriminator in order to provide a stronger guarantee to the generator's output. | background |
264bdb348c13f167768fd859b047e8_1 | However, the HRED system suffers from lack of diversity and does not support any guarantees on the generator output since the output conditional probability is not calibrated. <cite>Olabiyi et al. [7]</cite> tackle this problem by training a modified HRED generator alongside an adversarial discriminator in order to provide a stronger guarantee to the generator's output. | background |
264bdb348c13f167768fd859b047e8_2 | However, the HRED system suffers from lack of diversity and does not support any guarantees on the generator output since the output conditional probability is not calibrated. <cite>Olabiyi et al. [7]</cite> tackle this problem by training a modified HRED generator alongside an adversarial discriminator in order to provide a stronger guarantee to the generator's output. While the hredGAN system improves upon response quality, it does not capture speaker and other attributes modalities within a dataset and fails to generate persona-specific responses in datasets with multiple modalities. | motivation background |
264bdb348c13f167768fd859b047e8_3 | <cite>Olabiyi et al. [7]</cite> tackle this problem by training a modified HRED generator alongside an adversarial discriminator in order to provide a stronger guarantee to the generator's output. While the hredGAN system improves upon response quality, it does not capture speaker and other attributes modalities within a dataset and fails to generate persona-specific responses in datasets with multiple modalities. At the same time, there has been some recent work on introducing persona into dialogue models. | background |
264bdb348c13f167768fd859b047e8_4 | To overcome these limitations, we propose phredGAN , a multi-modal hredGAN dialogue system which additionally conditions the adversarial framework proposed by <cite>Olabiyi et al. [7]</cite> on speaker and/or utterance attributes in order to maintain response quality of hredGAN and still capture speaker and other modalities within a conversation. | extends uses |
264bdb348c13f167768fd859b047e8_5 | We train and sample the proposed phredGAN similar to the procedure for hredGAN <cite>[7]</cite> . | uses similarities |
264bdb348c13f167768fd859b047e8_6 | We train and sample the proposed phredGAN similar to the procedure for hredGAN <cite>[7]</cite> . We demonstrate system superiority over hredGAN and the state-of-the-art persona conversational model in terms | extends differences |
264bdb348c13f167768fd859b047e8_7 | We demonstrate system superiority over hredGAN and the state-of-the-art persona conversational model in terms | differences |
264bdb348c13f167768fd859b047e8_8 | The hredGAN proposed by Olabiyi et. al <cite>[7]</cite> contains three major components. | background |
264bdb348c13f167768fd859b047e8_9 | In the case of hredGAN <cite>[7]</cite> , it is a bidirectional RNN that discriminates at the word level to capture both the syntactic and semantic difference between the ground truth and the generator output. | background |
264bdb348c13f167768fd859b047e8_10 | Problem Formulation: The hredGAN <cite>[7]</cite> formulates multi-turn dialogue response generation as: given a dialogue history of sequence of utterances, where T i is the number of generated tokens. The framework uses a conditional GAN structure to learn a mapping from an observed dialogue history to a sequence of output tokens. | background |
264bdb348c13f167768fd859b047e8_11 | The generator, G, is trained to produce sequences that cannot be distinguished from the ground truth by an adversarially trained discriminator, D, akin to a two-player min-max optimization problem. The generator is also trained to minimize the cross-entropy loss L MLE (G) between the ground truth X i+1 , and the generator output Y i . The following objective summarizes both goals: where 位 G and 位 M are hyperparameters and L cGAN (G, D) and L MLE (G) are defined in Eqs. (5) and (7) of <cite>[7]</cite> respectively. | background |
264bdb348c13f167768fd859b047e8_12 | The proposed architecture of phredGAN is very similar to that of hredGAN summarized above. | similarities |
264bdb348c13f167768fd859b047e8_13 | The proposed architecture of phredGAN is very similar to that of hredGAN summarized above. The only difference is that the dialogue history is now X i = (X 1 , C 1 ), (X 2 , C 2 ), 路 路 路 , (X i , C i ) where C i is additional input that represents the speaker and/or utterance attributes. | extends differences |
264bdb348c13f167768fd859b047e8_14 | Discriminator: In addition to the D RN N in the discriminator of hredGAN , if the attribute C i+1 is a sequence of tokens, then the same tattRN N is used to summarize the attribute token embeddings; otherwise the single attribute embedding is concatenated with the other inputs of D RN N in Fig. 1 of <cite>[7]</cite> . | background |
264bdb348c13f167768fd859b047e8_15 | The modified system is as follows: Discriminator: In addition to the D RN N in the discriminator of hredGAN , if the attribute C i+1 is a sequence of tokens, then the same tattRN N is used to summarize the attribute token embeddings; otherwise the single attribute embedding is concatenated with the other inputs of D RN N in Fig. 1 of <cite>[7]</cite> . | uses |
264bdb348c13f167768fd859b047e8_16 | Noise Injection: Although <cite>[7]</cite> demonstrated that injecting noise at the word level seems to perform better than at the utterance level for hredGAN , we found that this is datasetdependent for phredGAN . | differences |
264bdb348c13f167768fd859b047e8_17 | The modified system is as follows: Noise Injection: Although <cite>[7]</cite> demonstrated that injecting noise at the word level seems to perform better than at the utterance level for hredGAN , we found that this is datasetdependent for phredGAN . | extends |
264bdb348c13f167768fd859b047e8_18 | We train both the generator and the discriminator (with a shared encoder) of phredGAN using the same training procedure in Algorithm 1 with 位 G = 位 M = 1 <cite>[7]</cite> . | uses |
264bdb348c13f167768fd859b047e8_19 | The number of attributes, V c is dataset-dependent Compute the generator output similar to Eq. (11) in <cite>[7]</cite> . | similarities uses |
264bdb348c13f167768fd859b047e8_20 | The parameter update is conditioned on the discriminator accuracy performance as in <cite>[7]</cite> with acc D th = 0.99 and acc G th = 0.75. | uses |
264bdb348c13f167768fd859b047e8_21 | For the modified noise sample, we perform a linear search for 伪 with sample size L = 1 based on the average discriminator loss, 鈭抣ogD(G(.)) <cite>[7]</cite> using trained models run in autoregressive mode to reflect performance in actual deployment. | uses |
264bdb348c13f167768fd859b047e8_22 | In this section, we explore phredGAN 's results on two conversational datasets and compare its performance to the persona system in Li et al. [8] and hredGAN <cite>[7]</cite> in terms of quantitative and qualitative measures. | similarities differences |
264bdb348c13f167768fd859b047e8_23 | We follow the same training, development, and test split as the UDC dataset in <cite>[7]</cite> , with 90%, 5%, and 5% proportions, respectively. | uses |
264bdb348c13f167768fd859b047e8_24 | We use similar evaluation metrics as in <cite>[7]</cite> including perplexity, BLEU [15] , ROUGE [16] , and distinct n-gram [17] scores. | uses |
264bdb348c13f167768fd859b047e8_25 | We also compare our system to hredGAN from <cite>[7]</cite> in terms of perplexity, ROGUE, and distinct n-grams scores. | similarities differences |
264bdb348c13f167768fd859b047e8_26 | In <cite>[7]</cite> , the authors recommend the version with word-level noise injection, hredGAN w , so we use this version in our comparison. | uses |
264bdb348c13f167768fd859b047e8_27 | Also for fair comparison, we use the same UDC dataset as reported in <cite>[7]</cite> . | uses |
26b00c6e5b499eea30e9cef0bbaf9f_0 | Though Baroni et al. (2014) suggested that predictive models which use neural networks to generate the distributed word representations (also known as embeddings in this context) outperform counting models which work on co-occurrence matrices, recent work shows evidence to the contrary (Levy et al., 2014;<cite> Salle et al., 2016)</cite> . | motivation |
26b00c6e5b499eea30e9cef0bbaf9f_1 | In this paper, we focus on improving a state-ofthe-art counting model, LexVec <cite>(Salle et al., 2016)</cite> , which performs factorization of the positive pointwise mutual information (PPMI) matrix using window sampling and negative sampling (WSNS). | uses |
26b00c6e5b499eea30e9cef0bbaf9f_2 | Though Baroni et al. (2014) suggested that predictive models which use neural networks to generate the distributed word representations (also known as embeddings in this context) outperform counting models which work on co-occurrence matrices, recent work shows evidence to the contrary (Levy et al., 2014;<cite> Salle et al., 2016)</cite> . In this paper, we focus on improving a state-ofthe-art counting model, LexVec <cite>(Salle et al., 2016)</cite> , which performs factorization of the positive pointwise mutual information (PPMI) matrix using window sampling and negative sampling (WSNS). | motivation |
26b00c6e5b499eea30e9cef0bbaf9f_3 | P n is the distribution used for drawing negative samples, chosen to be with 伪 = 3/4 (Mikolov et al., 2013b;<cite> Salle et al., 2016)</cite> , and #(w) the unigram frequency of w. Two methods were defined for the minimization of eqs. (2) and (3): Mini-batch and Stochastic <cite>(Salle et al., 2016)</cite> . | uses |
26b00c6e5b499eea30e9cef0bbaf9f_4 | with 伪 = 3/4 (Mikolov et al., 2013b;<cite> Salle et al., 2016)</cite> , and #(w) the unigram frequency of w. Two methods were defined for the minimization of eqs. (2) and (3): Mini-batch and Stochastic <cite>(Salle et al., 2016)</cite> . Since the latter is more computationally efficient and yields equivalent results, we adopt it in this paper. | uses |
26b00c6e5b499eea30e9cef0bbaf9f_5 | As suggested by Levy et al. (2015) and<cite> Salle et al. (2016)</cite> , positional contexts (introduced in Levy et al. (2014) ) are a potential solution to poor performance on syntactic analogy tasks. | background motivation |
26b00c6e5b499eea30e9cef0bbaf9f_6 | We report results from<cite> Salle et al. (2016)</cite> and use the same training corpus and parameters to train LexVec with positional contexts and external memory. | uses |
26b00c6e5b499eea30e9cef0bbaf9f_7 | As recommended in Levy et al. (2015) and used in<cite> Salle et al. (2016)</cite> , the PPMI matrix used in all LexVec models and in PPMI-SVD is transformed using context distribution smoothing exponentiating context frequencies to the power 0.75. | uses |
26b00c6e5b499eea30e9cef0bbaf9f_8 | Therefore, we perform the exact same evaluation as<cite> Salle et al. (2016)</cite> , namely the WS-353 Similarity (WSim) and Relatedness (WRel) (Finkelstein et al., 2001) , MEN (Bruni et al., 2012) , MTurk (Radinsky et al., 2011) , RW (Luong et al., 2013) , SimLex-999 (Hill et al., 2015) , MC (Miller and Charles, 1991) , RG (Rubenstein and Goodenough, 1965) , and SCWS (Huang et al., 2012) word similarity tasks 1 , and the Google semantic (GSem) and syntactic (GSyn) analogy (Mikolov et al., 2013a) and MSR syntactic analogy dataset (Mikolov et al., 2013c) tasks. | uses |
26fbf9f4ae740513d8889160ad9f63_0 | Several work have shown that discourse relations can improve the results of summarization in the case of factual texts or news articles (e.g.<cite> (Otterbacher et al., 2002)</cite> ). | background |
26fbf9f4ae740513d8889160ad9f63_1 | In particular,<cite> (Otterbacher et al., 2002)</cite> experimentally showed that discourse relations can improve the coherence of multi-document summaries. | background |
26fbf9f4ae740513d8889160ad9f63_2 | The comparison, contingency, and illustration relations are also considered by most of the work in the field of discourse analysis such as the PDTB: Penn Discourse TreeBank research group <cite>(Prasad et al., 2008)</cite> and the RST Discourse Treebank research group (Carlson and Marcu, 2001 ). | background |
26fbf9f4ae740513d8889160ad9f63_3 | From our corpus analysis, we have identified the six most prevalent discourse relations in this blog dataset, namely comparison, contingency, illustration, attribution, topic-opinion, and attributive. The comparison, contingency, and illustration relations are also considered by most of the work in the field of discourse analysis such as the PDTB: Penn Discourse TreeBank research group <cite>(Prasad et al., 2008)</cite> and the RST Discourse Treebank research group (Carlson and Marcu, 2001 ). | similarities |
26fbf9f4ae740513d8889160ad9f63_4 | The comparison, contingency, and illustration relations are also considered by most of the work in the field of discourse analysis such as the PDTB: Penn Discourse TreeBank research group <cite>(Prasad et al., 2008)</cite> and the RST Discourse Treebank research group (Carlson and Marcu, 2001 ). We considered three additional classes of relations: attributive, attribution, and topic-opinion. | extends differences |
26fbf9f4ae740513d8889160ad9f63_5 | For example: "Allied Capital is a closed-end management investment company that will operate as a business development concern." As shown in Figure 1 , illustration relations can be sub-divided into sub-categories: joint, list, disjoint, and elaboration relations according to the RST Discourse Treebank (Carlson and Marcu, 2001 ) and the Penn Discourse TreeBank <cite>(Prasad et al., 2008)</cite> . | background |
26fbf9f4ae740513d8889160ad9f63_6 | As shown in Figure 1 , the contingency relation subsumes several more specific relations: explanation, evidence, reason, cause, result, consequence, background, condition, hypothetical, enablement, and purpose relations according to the Penn Discourse TreeBank <cite>(Prasad et al., 2008)</cite> . | background |
26fbf9f4ae740513d8889160ad9f63_7 | The comparison relation subsumes the contrast relation according to the Penn Discourse TreeBank <cite>(Prasad et al., 2008)</cite> and the analogy and preference relations according to the RST Discourse Treebank (Carlson and Marcu, 2001) . | background |
26fbf9f4ae740513d8889160ad9f63_8 | However, we have complemented this parser with three other approaches: (Jindal and Liu, 2006 )'s approach is used to identify intra-sentence comparison relations; we have designed a tagger based on (Fei et al., 2008) 's approach to identify topic-opinion relations; and we have proposed a new approach to tag attributive relations<cite> (Mithun, 2012)</cite> . | extends uses |
26fbf9f4ae740513d8889160ad9f63_9 | A description and evaluation of these approaches can be found in<cite> (Mithun, 2012)</cite> . | background |
26fbf9f4ae740513d8889160ad9f63_10 | However, we have complemented this parser with three other approaches: (Jindal and Liu, 2006 )'s approach is used to identify intra-sentence comparison relations; we have designed a tagger based on (Fei et al., 2008) 's approach to identify topic-opinion relations; and we have proposed a new approach to tag attributive relations<cite> (Mithun, 2012)</cite> . A description and evaluation of these approaches can be found in<cite> (Mithun, 2012)</cite> . | uses |
26fbf9f4ae740513d8889160ad9f63_11 | To measure the usefulness of discourse relations for the summarization of informal texts, we have tested the effect of each relation with four different summarizers: BlogSum<cite> (Mithun, 2012)</cite> , MEAD<cite> (Radev et al., 2004)</cite> , the best scoring system at TAC 2008 5 and the best scoring system at DUC 2007 6 . | uses |
26fbf9f4ae740513d8889160ad9f63_12 | To measure the usefulness of discourse relations for the summarization of informal texts, we have tested the effect of each relation with four different summarizers: BlogSum<cite> (Mithun, 2012)</cite> , MEAD<cite> (Radev et al., 2004)</cite> , the best scoring system at TAC 2008 5 and the best scoring system at DUC 2007 6 . Finally the most appropriate schema is selected based on a given question type; and candidate sentences fill particular slots in the selected schema based on which discourse relations they contain in order to create the final summary (details of BlogSum can be found in<cite> (Mithun, 2012)</cite> ). | uses background |
26fbf9f4ae740513d8889160ad9f63_13 | Finally the most appropriate schema is selected based on a given question type; and candidate sentences fill particular slots in the selected schema based on which discourse relations they contain in order to create the final summary (details of BlogSum can be found in<cite> (Mithun, 2012)</cite> ). | background |
26fbf9f4ae740513d8889160ad9f63_14 | To ensure that the results were not specific to our summarizer, we performed the same experiments with two other systems: the MEAD summarizer<cite> (Radev et al., 2004)</cite> , a publicly available and a widely used summarizer, and with the output of the TAC best-scoring system. | uses |
27dbdd4827554df0f53013966242dc_0 | Our work is based on the SummaRuNNer model <cite>[5]</cite> . It consists of a two-layer bi-directional Gated Recurrent Unit (GRU) Recurrent Neural Network (RNN) which treats the summarization problem as a binary sequence classification problem, where each sentence is classified sequentially as sentence to be included or not in the summary. However, we introduced two modifications to the original SummaRuNNer architecture, leading to better results while reducing complexity: arXiv:1911.06121v1 [cs.CL] 13 Nov 2019 Fig. 1 . | extends |
27dbdd4827554df0f53013966242dc_2 | In contrast to <cite>[5]</cite> , we trained our model only on CNN articles from the CNN/Daily Mail corpus [2] . | differences |
27dbdd4827554df0f53013966242dc_3 | In a similar approach to <cite>[5]</cite> , we calculated the ROUGE-1 F1 score between each sentence and its article's abstractive summary. | similarities |
27ee0fbed3a88854ebe945dfffefd8_0 | A review of the methods in the article <cite>[35]</cite> about the recognition of timexes for English and Spanish has shown a certain shift within the most popular solutions. | uses |
27ee0fbed3a88854ebe945dfffefd8_1 | The best systems listed in <cite>[35]</cite> , called TIPSem [16] and ClearTK [1] , use CRFs for recognition, so initially, we decided to apply the CRF-based approach for this task. | uses |
27ee0fbed3a88854ebe945dfffefd8_2 | Experiments were carried out by the method proposed in <cite>[35]</cite> . | uses |
27ee0fbed3a88854ebe945dfffefd8_3 | Then we evaluated these results using more detailed measures for timexes, presented in <cite>[35]</cite> . | uses |
27ee0fbed3a88854ebe945dfffefd8_5 | If there was an overlap, a relaxed type F1-score (Type.F1) was calculated <cite>[35]</cite> . | uses |
27ee0fbed3a88854ebe945dfffefd8_6 | Then we evaluated these results using more detailed measures for timexes, presented in <cite>[35]</cite> . F1) evaluation has also been carried out to determine whether there is an overlap between the system entity and gold entity, e.g. [Sunday] and [Sunday morning] <cite>[35]</cite> . | uses |
27ee0fbed3a88854ebe945dfffefd8_7 | Table 9 : Evaluation results for all TIMEX3 classes (total) for 9 word embeddings models (3 best models from each embeddings group: EE, EP, EC from Table 8 ) using the following measures from <cite>[35]</cite> : strict precision, strict recall, strict F1-score, relaxed precision, relaxed recall, relaxed F1-score, type F1-score. | uses |
28038a4fa4182ccdc6134f2138c0da_0 | The task of definition modeling, introduced by <cite>Noraset et al. (2017)</cite> , consists in generating the dictionary definition of a specific word: for instance, given the word "monotreme" as input, the system would need to produce a definition such as "any of an order (Monotremata) of egg-laying mammals comprising the platypuses and echidnas". | background |
28038a4fa4182ccdc6134f2138c0da_1 | A major intended application of definition modeling is the explication and evaluation of distributed lexical representations, also known as word embeddings<cite> (Noraset et al., 2017)</cite> . | background |
28038a4fa4182ccdc6134f2138c0da_2 | In their seminal work on definition modeling, <cite>Noraset et al. (2017)</cite> likened systems generating definitions to language models, which can naturally be used to generate arbitrary text. | background |
28038a4fa4182ccdc6134f2138c0da_3 | This reformulation can appear contrary to the original proposal by <cite>Noraset et al. (2017)</cite> , which conceived definition modeling as a "word-tosequence task". | background |
28038a4fa4182ccdc6134f2138c0da_4 | Though different kinds of linguistic contexts have been suggested throughout the literature, we remark here that sentential context may sometimes suffice to guess the meaning of a word that we don't know (Lazaridou et al., 2017) . Quoting from the example above, the context "enough around-let's get back to work!" sufficiently characterizes the meaning of the omitted verb to allow for an approximate definition for it even if the blank is not filled (Taylor, 1953; Devlin et al., 2018) . This reformulation can appear contrary to the original proposal by <cite>Noraset et al. (2017)</cite> , which conceived definition modeling as a "word-tosequence task". | differences |
28038a4fa4182ccdc6134f2138c0da_5 | Despite some key differences, all of the previously proposed architectures we are aware of<cite> (Noraset et al., 2017</cite>; Gadetsky et al., 2018; followed a pattern similar to sequence-to-sequence models. | similarities |
28038a4fa4182ccdc6134f2138c0da_6 | In the case of <cite>Noraset et al. (2017)</cite> , the encoding was the concatenation of the embedding of the definiendum, a vector representation of its sequence of characters derived from a characterlevel CNN, and its "hypernym embedding". | background |
28038a4fa4182ccdc6134f2138c0da_7 | Should we mark the definiendum before encoding, then only the definiendum embedding is passed into the encoder: the resulting system provides out-of-context definitions, like in <cite>Noraset et al. (2017)</cite> where the definition is not linked to the context of a word but to its definiendum only. | similarities |
28038a4fa4182ccdc6134f2138c0da_8 | 4 The dropout rate and warmup steps number were set using a hyperparameter search on the dataset from <cite>Noraset et al. (2017)</cite> , during which encoder and decoder vocabulary were merged for computational simplicity and models stopped after 12,000 steps. | uses |
28038a4fa4182ccdc6134f2138c0da_9 | The dataset of <cite>Noraset et al. (2017)</cite> (henceforth D Nor ) maps definienda to their respective definientia, as well as additional information not used here. | differences |
28038a4fa4182ccdc6134f2138c0da_10 | We train our models on three distinct datasets, which are all borrowed or adapted from previous works on definition modeling. The dataset of <cite>Noraset et al. (2017)</cite> (henceforth D Nor ) maps definienda to their respective definientia, as well as additional information not used here. | uses |
28038a4fa4182ccdc6134f2138c0da_11 | Perplexity measures for <cite>Noraset et al. (2017)</cite> and Gadetsky et al. (2018) are taken from the authors' respective publications. | background |