id
stringlengths 32
33
| x
stringlengths 41
1.75k
| y
stringlengths 4
39
|
|---|---|---|
ef742defff1c2bdf145f72796cf3af_16 | We use the software provided by<cite> Jansen et al. (2014)</cite> 7 to extract the discourse features described in Section 4 and referred to as x ext in Section 3. | uses |
ef742defff1c2bdf145f72796cf3af_17 | Following<cite> Jansen et al. (2014)</cite> , we train them using the skip-gram model (Mikolov et al., 2013) We use the L6 Yahoo dataset 8 to train the skip-gram model for the YA dataset and the Ask Ubuntu September 2015 data dump for the AU dataset. | uses |
ef742defff1c2bdf145f72796cf3af_18 | They also perform better than the approach of<cite> Jansen et al. (2014)</cite> who used SVMrank with a linear kernel. | differences |
ef742defff1c2bdf145f72796cf3af_19 | On the YA dataset, the results are better than<cite> Jansen et al. (2014)</cite> and very similar to Bogdanova and Foster (2016) . | differences |
ef742defff1c2bdf145f72796cf3af_20 | As external features, we evaluate the discourse features that were found useful for this task by<cite> Jansen et al. (2014)</cite> . | uses |
f2925513a7cce2e80ade1f948164d0_0 | In the domain of text, many modern approaches often begin by embedding the input text data into an embedding space that is used as the first layer in a subsequent deep network [4] , [14] . These word embeddings have been shown to contain the same biases <cite>[3]</cite> , due to the source data from which they are trained. In effect, biases from the source data, such as in the differences in representation for men and women, that have been found in many different large-scale studies [5] , [10] , [12] , carry through to the semantic relations in the word embeddings, which become baked into the learning systems that are built on top of them. | motivation background |
f2925513a7cce2e80ade1f948164d0_1 | First we propose a new version of the Word Embedding Association Tests (WEATs) studied in <cite>[3]</cite> , designed to demonstrate and quantify bias in word embeddings, which puts them on a firm foundation by using the Linguistic Inquiry and Word Count (LIWC) lexica [17] to systematically detect and measure embedding biases. With this improved experimental setting, we find that European-American names are viewed more positively than African-American names, male names are more associated with work while female names are more associated with family, and that the academic disciplines of science and maths are more associated with male terms than the arts, which are more associated with female terms. Using this new methodology, we then find that there is a gender bias in the way different occupations are represented by the embedding. | motivation background differences |
f2925513a7cce2e80ade1f948164d0_2 | We first propose a new version of the Word Embedding Association Tests studied in <cite>[3]</cite> by using the LIWC lexica to systematically detect and measure the biases within the embedding, keeping the tests comparable with the same set of target words. We further extend this work using additional sets of target words, and compare sentiment across male and female names. Furthermore, we investigate gender bias in words that represent different occupations, comparing these associations with UK national employment statistics. | extends background |
f2925513a7cce2e80ade1f948164d0_3 | We begin by using the target words from <cite>[3]</cite> which were originally used in [8] , allowing us to directly compare our findings with the original WEAT. Our approach differs from that of <cite>[3]</cite> in that while we use the same set of target words in each test, we use an expanded set of attribute words, allowing us to perform a more rigorous, systematic study of the associations found within the word embeddings. For this, we use attribute words sourced from the LIWC lexica [17] . | extends |
f2925513a7cce2e80ade1f948164d0_4 | For each of the original word categories used in <cite>[3]</cite> , we matched them with their closest equivalent within the LIWC categories, for example matching the word lists for 'career' and 'family' with the 'work' and 'family' LIWC categories. We tested the association between each target word and the set of attribute words using the method described in Sec. II-B, focussing on the differences in association between sentimental terms and European-and African-American names, subject disciplines to each of the genders, career and family terms with gendered names, as well as looking at the association between gender and sentiment. | extends |
f2925513a7cce2e80ade1f948164d0_5 | Taking the list of target European-American and African-American names used in <cite>[3]</cite> , we tested each of them for their associated with the positive and negative emotion concepts found in [17] by using the methodology described by Eq. 3 in Sec. II-B, replacing the short list of words used to originally represent pleasant and unpleasant attribute sets. | uses |
f2925513a7cce2e80ade1f948164d0_6 | Our test found that while both European-American names and African-American names are more associated with positive emotions than negative emotions, the test showed that European-American names are more associated with positive emotions than their African-American counterparts, as shown in Fig. 1a . This finding supports the association test in <cite>[3]</cite> , where they also found that European-American names were more pleasant than African-American names. | similarities |
f2925513a7cce2e80ade1f948164d0_8 | 3) Association of Gender with Career and Family : Taking the list of target gendered names used in <cite>[3]</cite> , we tested each of them for their associated with the career and family concepts using the categories of 'work' and 'family' found in LIWC [17] . | uses |
f2925513a7cce2e80ade1f948164d0_9 | As shown in Fig. 1c , we found that the set of male names was more associated with the concept of work, while the female names were more associated with family, mirroring the results found in <cite>[3]</cite> . Extending this test, we generated a much larger set of male and female target names from an online list of baby names 1 . Repeating the same test on this larger set of names, we found that male and female names were much less separated than suggested by previous results, with only minor differences between the two, as shown in Fig. 1d . | extends |
f2925513a7cce2e80ade1f948164d0_10 | We found that there is a strong, significant correlation (ρ = 0.57, p-value < 10 −6 ) between the word embedding association between gender and occupation and the number of people of each gender in the United Kingdom working in those roles. This supports a similar finding for U.S. employment statistics using an independent set of occupations found in <cite>[3]</cite> . | background similarities |
f2925513a7cce2e80ade1f948164d0_12 | In this paper, we have introduced the LIWC-WEAT, a set of objective tests extending the association tests in <cite>[3]</cite> by using the LIWC lexica to measure bias within word embeddings. | motivation background |
f2925513a7cce2e80ade1f948164d0_13 | We found bias in both the associations of gender and race, as first described in <cite>[3]</cite> , while additionally finding that male names have a slightly higher positive association than female names. Biases found in the embedding were also shown to reflect biases in the real world and the media, where we found a correlation between the number of men and women in an occupation and its association with each set of male and female names. | differences background |
f2db88c0d4e0ec4c34fc295a5d59ba_1 | These constraints can be lexicalized (Collins, 1999; Charniak, 2000) , unlexicalized (Johnson, 1998; Klein and Manning, 2003b) or automatically learned (Matsuzaki et al., 2005; <cite>Petrov et al., 2006)</cite> . | background |
f2db88c0d4e0ec4c34fc295a5d59ba_4 | Computing the joint likelihood of the observed parse trees T and sentences w requires summing over all derivations t over split subcategories: Matsuzaki et al. (2005) derive an EM algorithm for maximizing the joint likelihood, and<cite> Petrov et al. (2006)</cite> extend this algorithm to use a split&merge procedure to adaptively determine the optimal number of subcategories for each observed category. | background |
f2db88c0d4e0ec4c34fc295a5d59ba_5 | While the split&merge procedure described above is shown in<cite> Petrov et al. (2006)</cite> to reduce the variance in final performance, we found after closer examination that there are substantial differences in the patterns learned by the grammars. | differences |
f2db88c0d4e0ec4c34fc295a5d59ba_6 | In previous work<cite> (Petrov et al., 2006</cite>; Petrov and Klein, 2007 ) the final grammar was chosen based on its performance on a held-out set (section 22), and corresponds to the second best grammar in Figure 3 (because only 8 different grammars were trained). | background |
f2db88c0d4e0ec4c34fc295a5d59ba_7 | Using weights learned on a held-out set and rescoring 50-best lists from Charniak (2000) and<cite> Petrov et al. (2006)</cite> , they obtain an F 1 score of 91.0 (which they further improve to 91.4 using a voting scheme). | background |
f2db88c0d4e0ec4c34fc295a5d59ba_8 | The parameters of each latent variable grammar are typically smoothed in a linear fashion to prevent excessive overfitting<cite> (Petrov et al., 2006)</cite> . | background |
f2db88c0d4e0ec4c34fc295a5d59ba_9 | It is also interesting to note that the best results in Zhang et al. (2009) are achieved by combining kbest lists from a latent variable grammar of<cite> Petrov et al. (2006)</cite> with the self-trained reranking parser of McClosky et al. (2006) . | background |
f2ff155003d139b3677f746baf3807_0 | In this paper, we followed the line of predicting ICD codes from unstructured text of the MIMIC dataset (Johnson et al. 2016 ), because it is widely studied and publicly available. The state-of-the-art model for this line of work is the combination of the convolutional neural network (CNN) and the attention mechanism<cite> (Mullenbach et al. 2018)</cite> . However, this model only contains one convolutional layer to build document representations for subsequent layers to predict ICD codes. | motivation background |
f2ff155003d139b3677f746baf3807_1 | Our Mul-arXiv:1912.00862v1 [cs.CL] 25 Nov 2019 tiResCNN model is composed of five layers: the input layer leverages word embeddings pre-trained by word2vec (Mikolov et al. 2013) ; the multi-filter convolutional layer consists of multiple convolutional filters (Kim 2014); the residual convolutional layer contains multiple residual blocks (He et al. 2016) ; the attention layer keeps the interpretability for the model following<cite> (Mullenbach et al. 2018)</cite> ; the output layer utilizes the sigmoid function to predict the probability of each ICD code. | similarities background |
f2ff155003d139b3677f746baf3807_2 | To evaluate our model, we employed the MIMIC dataset (Johnson et al. 2016 ) which has been widely used for automated ICD coding. Compared with 5 existing and stateof-the-art models (Perotte et al. 2013; Prakash et al. 2017; Shi et al. 2017; Baumel et al. 2018;<cite> Mullenbach et al. 2018)</cite> , our model outperformed them in nearly all the evaluation metrics (i.e., macro-and micro-AUC, macro-and micro-F1, precision at K). | background |
f2ff155003d139b3677f746baf3807_3 | <cite>Mullenbach et al. (2018)</cite> incorporated the convolutional neural network (CNN) with per-label attention mechanism. Their model achieved the state-of-the-art performance among the work using only unstructured text of the MIMIC dataset. | background |
f2ff155003d139b3677f746baf3807_4 | Following <cite>Mullenbach et al. (2018)</cite> , we employed the perlabel attention mechanism to make each ICD code attend to different parts of the document representation H. The attention layer is formalized as: where U ∈ R (m×d p )×l represents the parameter matrix of the attention layer, A ∈ R n×l represents the attention weights for each pair of an ICD code and a word, V ∈ R l×(m×d p ) represents the output of the attention layer. | uses |
f2ff155003d139b3677f746baf3807_5 | For training, we treated the ICD coding task as a multi-label classification problem following previous work (McCallum 1999;<cite> Mullenbach et al. 2018)</cite> . The training objective is to minimize the binary cross entropy loss between the predictionỹ and the target y: where w denotes the input word sequence and θ denotes all the parameters. | uses |
f2ff155003d139b3677f746baf3807_6 | Following <cite>Mullenbach et al. (2018)</cite> , we used discharge summaries, split them by patient IDs, and conducted experiments using the full codes as well as the top-50 most frequent codes. | uses |
f2ff155003d139b3677f746baf3807_8 | Preprocessing Following previous work<cite> (Mullenbach et al. 2018)</cite> , the text was tokenized, and each token were transformed into its lowercase. The tokens that contain no alphabetic characters were removed such as numbers and punctuations. The maximum length of a token sequence is 2,500 and the one that exceeds this length will be truncated. | uses |
f2ff155003d139b3677f746baf3807_9 | Since our model has a number of hyper-parameters, it is infeasible to search optimal values for all hyper-parameters. Therefore, some hyper-parameter values were chosen empirically or following prior work<cite> (Mullenbach et al. 2018</cite> ). | uses background |
f2ff155003d139b3677f746baf3807_10 | • CNN, which only has one convolutional filter and is equivalent to the CAML model<cite> (Mullenbach et al. 2018</cite> ). | uses |
f2ff155003d139b3677f746baf3807_11 | CAML & DR-CAML The Convolutional Attention network for Multi-Label classification (CAML) was proposed by <cite>Mullenbach et al. (2018)</cite> . It has achieved the state-of-theart results on the MIMIC-III and MIMIC-II datasets among the models using unstructured text. It consists of one convolutional layer and one attention layer to generate label-aware features for multi-label classification (McCallum 1999). The Description Regularized CAML (DR-CAML) is an extension of CAML and incorporates the text description of each code to regularize the model. | background |
f2ff155003d139b3677f746baf3807_13 | For CAML, we used the optimal hyper-parameter setting reported in their paper<cite> (Mullenbach et al. 2018)</cite> . | uses |
f3012301e42a4075ed6d4d2b39b528_0 | The past work in sarcasm detection involves rule-based and statistical approaches using: (a) unigrams and pragmatic features (such as emoticons, etc.) (Gonzalez-Ibanez et al., 2011; Carvalho et al., 2009; Barbieri et al., 2014) , (b) extraction of common patterns, such as hashtag-based sentiment (Maynard and Greenwood, 2014; Liebrecht et al., 2013) , a positive verb being followed by a negative situation<cite> (Riloff et al., 2013)</cite> , or discriminative n-grams (Tsur et al., 2010a; Davidov et al., 2010) . | background |
f3012301e42a4075ed6d4d2b39b528_1 | • Our sarcasm detection system outperforms two state-of-art sarcasm detection systems <cite>(Riloff et al., 2013</cite>; Maynard and Greenwood, 2014) . | differences |
f3012301e42a4075ed6d4d2b39b528_2 | Our feature engineering is based on<cite> Riloff et al. (2013)</cite> and Ramteke et al. (2013) . | similarities uses |
f3012301e42a4075ed6d4d2b39b528_3 | For this, we modify the algorithm given in<cite> Riloff et al. (2013)</cite> in two ways: (a) they extract only positive verbs and negative noun situation phrases. | extends differences |
f3012301e42a4075ed6d4d2b39b528_4 | 2. Tweet-B (2278 tweets, 506 sarcastic): This dataset was manually labeled for<cite> Riloff et al. (2013</cite> To extract the implicit incongruity features, we run the iterative algorithm described in Section 4.2, on a dataset of 4000 tweets (50% sarcastic) (also created using hashtag-based supervision). | uses similarities |
f3012301e42a4075ed6d4d2b39b528_5 | Table 2 : Comparative results for Tweet-A using rule-based algorithm and statistical classifiers using our feature combinations 6 Evaluation Table 2 shows the performance of our classifiers in terms of Precision (P), Recall (R) and F-score<cite> Riloff et al. (2013)</cite> 's two rule-based algorithms: the ordered version predicts a tweet as sarcastic if it has a positive verb phrase followed by a negative situation/noun phrase, while the unordered does so if the two are present in any order. We see that all statistical classifiers surpass the rule-based algorithms. | differences |
f3012301e42a4075ed6d4d2b39b528_6 | This is an improvement of about 5% over the baseline, and 40% over the algorithm by<cite> Riloff et al. (2013)</cite> . | extends differences |
f3012301e42a4075ed6d4d2b39b528_7 | Table 4 shows that we achieve a 10% higher F-score than the best reported F-score of<cite> Riloff et al. (2013)</cite> . | differences |
f3012301e42a4075ed6d4d2b39b528_8 | Our system also outperforms two past works <cite>(Riloff et al., 2013</cite>; Maynard and Greenwood, 2014) with 10-20% improvement in F-score. | differences |
f3282df3adadf78320e99c09d8384f_0 | Following <cite>Gong et al. (2018)</cite> , we consider two document collections heterogeneous if <cite>their</cite> documents differ systematically with respect to vocabulary and / or level of abstraction. With these defining differences, there often also comes a difference in length, which, however, by itself does not make document collections heterogeneous. | background motivation |
f3282df3adadf78320e99c09d8384f_1 | We demonstrate our method with the Concept-Project matching task (<cite>Gong et al. (2018)</cite> ), which is described in the next section. | uses |
f3282df3adadf78320e99c09d8384f_2 | The annotation was done by undergrad engineering students. <cite>Gong et al. (2018)</cite> do not provide any specification, or annotation guidelines, of the semantics of the 'matches' relation to be annotated. Instead, <cite>they</cite> create gold standard annotations based on a majority vote of three manual annotations. | differences |
f3282df3adadf78320e99c09d8384f_3 | The extent to which this information is used by <cite>Gong et al. (2018)</cite> is not entirely clear, so we experiment with several setups (cf. Section 4). | extends motivation |
f3282df3adadf78320e99c09d8384f_4 | **<cite>GONG ET AL. (2018)</cite>'S APPROACH** The approach by <cite>Gong et al. (2018)</cite> is based on the idea that the longer document in the pair is reduced to a set of topics which capture the essence of the document in a way that eliminates the effect of a potential length difference. | background |
f3282df3adadf78320e99c09d8384f_5 | <cite>Gong et al. (2018)</cite> motivate <cite>their</cite> approach mainly with the length mismatch argument, which <cite>they</cite> claim makes approaches relying on document representations (incl. vector averaging) unsuitable. | background |
f3282df3adadf78320e99c09d8384f_6 | Accordingly, <cite>they</cite> use Doc2Vec (Le and Mikolov (2014) ) as one of their baselines, and show that its performance is inferior to <cite>their</cite> method. | background |
f3282df3adadf78320e99c09d8384f_7 | <cite>They</cite> do not, however, provide a much simpler averaging-based baseline. | background |
f3282df3adadf78320e99c09d8384f_8 | As a second baseline, <cite>they</cite> use Word Mover's Distance (Kusner et al. (2015) ), which is based on word-level distances, rather than distance of global document representations, but which also fails to be competitive with <cite>their</cite> topic-based method. | background |
f3282df3adadf78320e99c09d8384f_9 | <cite>Gong et al. (2018)</cite> use two different sets of word embeddings: One (topic wiki) was trained on a full English Wikipedia dump, the other (wiki science) on a smaller subset of the former dump which only contained science articles. | background |
f3282df3adadf78320e99c09d8384f_10 | We implement this standard measure (AVG COS SIM) as a baseline for both our method and for the method by <cite>Gong et al. (2018)</cite> . | extends |
f3282df3adadf78320e99c09d8384f_11 | Parameter tuning experiments were performed on a random subset of 20% of our data set (54% positive). Note that <cite>Gong et al. (2018)</cite> used only 10% of <cite>their</cite> 537 instances data set as tuning data. | differences |
f3282df3adadf78320e99c09d8384f_12 | Since the original data split used by <cite>Gong et al. (2018)</cite> is unknown, we cannot exactly replicate <cite>their</cite> settings, but we also perform ten runs using randomly selected 10% of our 408 instances test data set, and report average P, R, F, and standard deviation. | differences |
f3282df3adadf78320e99c09d8384f_14 | Note that our Both setting is probably the one most similar to the concept input used by <cite>Gong et al. (2018)</cite> . | similarities |
f3282df3adadf78320e99c09d8384f_15 | This result corroborates our findings on the tuning data, and clearly contradicts the (implicit) claim made by <cite>Gong et al. (2018)</cite> regarding the infeasibility of document-level matching for documents of different lengths. | differences |
f3282df3adadf78320e99c09d8384f_16 | The second, more important finding is that our proposed TOP n COS SIM AVG measure is also very competitive, as it also outperforms both systems by <cite>Gong et al. (2018)</cite> in two out of three settings. | similarities |
f3282df3adadf78320e99c09d8384f_17 | 8 This is the more important as we exclusively employ off-the-shelf, general-purpose embeddings, while <cite>Gong et al. (2018)</cite> reach <cite>their</cite> best results with a much more sophisticated system and with embeddings that were custom-trained for the science domain. | differences |
f3282df3adadf78320e99c09d8384f_18 | Thus, while the performance of our proposed TOP n COS SIM AVG method is superior to the approach by <cite>Gong et al. (2018)</cite> , it is itself outperformed by the 'baseline' AVG COS SIM method with appropriate weighting. | differences |
f3282df3adadf78320e99c09d8384f_19 | We presented a simple method for semantic matching of documents from heterogeneous collections as a solution to the Concept-Project matching task by <cite>Gong et al. (2018)</cite> . | motivation |
f3282df3adadf78320e99c09d8384f_20 | Another result is that, contrary to the claim made by <cite>Gong et al. (2018)</cite> , the standard averaging approach does indeed work very well even for heterogeneous document collections, if appropriate weighting is applied. | differences |
f3f61d50929f862e263e3f658852bc_0 | Section 3 then empirically analyzes correlations in two recent argument corpora, one annotated for 15 well-defined quality dimensions taken from theory (Wachsmuth et al., 2017a) and one with 17 reasons for quality differences phrased spontaneously in practice<cite> (Habernal and Gurevych, 2016a)</cite> . | background |
f3f61d50929f862e263e3f658852bc_1 | Conv A is more convincing than B. Table 2 : The 17+1 practical reason labels given in the corpus of <cite>Habernal and Gurevych (2016a)</cite> . | background |
f3f61d50929f862e263e3f658852bc_2 | Without giving any guidelines, the authors also asked for reasons as to why A is more convincing than B. In a follow-up study<cite> (Habernal and Gurevych, 2016a)</cite> , these reasons were used to derive a hierarchical annotation scheme. | background |
f3f61d50929f862e263e3f658852bc_3 | 9111 argument pairs were then labeled with one or more of the 17 reason labels in Table 2 Negative Properties of Argument B Positive Properties of Argument A Quality Dimension 5-1 5-2 5-3 6-1 6-2 6-3 7-1 7-2 7-3 7-4 8-1 8-4 8-5 9-1 9-2 9-3 9- Wachsmuth et al. (2017a) given for each of the 17+1 reason labels of <cite>Habernal and Gurevych (2016a)</cite> . | background |
f3f61d50929f862e263e3f658852bc_4 | For Hypotheses 1 and 2, we consider all 736 pairs of arguments from <cite>Habernal and Gurevych (2016a)</cite> where both have been annotated by Wachsmuth et al. (2017a) . | similarities uses |
f3f61d50929f862e263e3f658852bc_5 | Besides, the descriptions of 6-2 and 6-3 sound like local but cor- Table 4 : The mean rating for each quality dimension of those arguments from Wachsmuth et al. (2017a) given for each reason label<cite> (Habernal and Gurevych, 2016a)</cite> . | background |
f3f61d50929f862e263e3f658852bc_6 | For explicitness, we computed the mean rating for each quality dimension of all arguments from Wachsmuth et al. (2017a) with a particular reason label from <cite>Habernal and Gurevych (2016a)</cite> . | similarities uses |
f3f61d50929f862e263e3f658852bc_7 | 3 Also, Table 4 reveals which reasons predict absolute differences most: The mean ratings of 7-3 (off-topic) are very low, indicating a strong negative impact, while 6-3 (irrelevant reasons) still shows rather 3 While the differences seem not very large, this is expected, as in many argument pairs from <cite>Habernal and Gurevych (2016a)</cite> both arguments are strong or weak respectively. | background |
f3f61d50929f862e263e3f658852bc_8 | Regarding simplification, the most common practical reasons of <cite>Habernal and Gurevych (2016a)</cite> imply what to focus on. | background |
f4becae9cd7eeaa7fd3085ff904aaa_0 | Recently, there has been much interest in applying neural network models to solve the problem, where little or no linguistic analysis is performed except for tokenization<cite> (Filippova et al., 2015</cite>; Rush et al., 2015; Chopra et al., 2016) . | background |
f4becae9cd7eeaa7fd3085ff904aaa_1 | For example,<cite> Filippova et al. (2015)</cite> used close to two Figure 1 : Examples of in-domain and out-ofdomain results by a standard abstractive sequenceto-sequence model trained on the Gigaword corpus. | background |
f4becae9cd7eeaa7fd3085ff904aaa_2 | Although neural network-based models have achieved good performance on this task recently, they tend to suffer from two problems: (1) They require a large amount of data for training. For example,<cite> Filippova et al. (2015)</cite> used close to two Figure 1 : Examples of in-domain and out-ofdomain results by a standard abstractive sequenceto-sequence model trained on the Gigaword corpus. | background motivation |
f4becae9cd7eeaa7fd3085ff904aaa_3 | To this end, we extend the deletionbased LSTM model for sentence compression by<cite> Filippova et al. (2015)</cite> . | extends |
f4becae9cd7eeaa7fd3085ff904aaa_4 | Specifically, we propose two major changes to the model by<cite> Filippova et al. (2015)</cite> : (1) We explicitly introduce POS embeddings and dependency relation embeddings into the neural network model. | extends |
f4becae9cd7eeaa7fd3085ff904aaa_5 | We evaluate our method using around 10,000 sentence pairs released by<cite> Filippova et al. (2015)</cite> and two other data sets representing out-ofdomain data. | uses |
f4becae9cd7eeaa7fd3085ff904aaa_6 | Our problem setup is the same as that by<cite> Filippova et al. (2015)</cite> . | uses |
f4becae9cd7eeaa7fd3085ff904aaa_7 | This base model is largely based on the model by<cite> Filippova et al. (2015)</cite> with some differences, which will be explained below. | uses differences |
f4becae9cd7eeaa7fd3085ff904aaa_8 | Following<cite> Filippova et al. (2015)</cite> , our bi-LSTM has three layers, as shown in Figure 2 . | uses |
f4becae9cd7eeaa7fd3085ff904aaa_9 | There are some differences between our base model and the LSTM model by<cite> Filippova et al. (2015)</cite> . | differences |
f4becae9cd7eeaa7fd3085ff904aaa_10 | (1)<cite> Filippova et al. (2015)</cite> first encoded the input sentence in its reverse order using the same LSTM before processing the sentence for sequence labeling. | background |
f4becae9cd7eeaa7fd3085ff904aaa_11 | There are some differences between our base model and the LSTM model by<cite> Filippova et al. (2015)</cite> . (1)<cite> Filippova et al. (2015)</cite> first encoded the input sentence in its reverse order using the same LSTM before processing the sentence for sequence labeling. | differences |
f4becae9cd7eeaa7fd3085ff904aaa_12 | (2)<cite> Filippova et al. (2015)</cite> used only a single-directional LSTM while we use bi-LSTM to capture contextual information from both directions. | differences |
f4becae9cd7eeaa7fd3085ff904aaa_13 | (3) Although<cite> Filippova et al. (2015)</cite> did not use any syntactic information in their basic model, they introduced some features based on dependency parse trees in their advanced models. | background |
f4becae9cd7eeaa7fd3085ff904aaa_14 | There are some differences between our base model and the LSTM model by<cite> Filippova et al. (2015)</cite> . (3) Although<cite> Filippova et al. (2015)</cite> did not use any syntactic information in their basic model, they introduced some features based on dependency parse trees in their advanced models. | differences |
f4becae9cd7eeaa7fd3085ff904aaa_15 | (4)<cite> Filippova et al. (2015)</cite> combined the predicted y i−1 with w i to help predict y i . | background |
f4becae9cd7eeaa7fd3085ff904aaa_16 | (4)<cite> Filippova et al. (2015)</cite> combined the predicted y i−1 with w i to help predict y i . This adds some dependency between consecutive labels. We do not do this because later we will introduce an ILP layer to introduce dependencies among labels. | differences |
f4becae9cd7eeaa7fd3085ff904aaa_17 | For example, the method above as well as the original method by<cite> Filippova et al. (2015)</cite> cannot impose any length constraint on the compressed sentences. | motivation |
f4becae9cd7eeaa7fd3085ff904aaa_18 | Google News: The first dataset contains 10,000 sentence pairs collected and released by<cite> Filippova et al. (2015)</cite> 2 . | uses |
f4becae9cd7eeaa7fd3085ff904aaa_19 | We compare our methods with a few baselines: LSTM: This is the basic LSTM-based deletion method proposed by<cite> (Filippova et al., 2015)</cite> . | uses |
f4becae9cd7eeaa7fd3085ff904aaa_20 | LSTM+: This is advanced version of the model proposed by<cite> Filippova et al. (2015)</cite> , where the authors incorporated some dependency parse tree information into the LSTM model and used the prediction on the previous word to help the prediction on the current word. | background |
f4becae9cd7eeaa7fd3085ff904aaa_21 | We compare our methods with a few baselines: LSTM: This is the basic LSTM-based deletion method proposed by<cite> (Filippova et al., 2015)</cite> . LSTM+: This is advanced version of the model proposed by<cite> Filippova et al. (2015)</cite> , where the authors incorporated some dependency parse tree information into the LSTM model and used the prediction on the previous word to help the prediction on the current word. | uses |
f4becae9cd7eeaa7fd3085ff904aaa_22 | We took the first 1,000 sentence pairs from Google News as the test set, following the same practice as<cite> Filippova et al. (2015)</cite> . | uses |
f4becae9cd7eeaa7fd3085ff904aaa_23 | (2) In the in-domain setting, with the same amount of training data (8,000), our BiLSTM method with syntactic features (BiLSTM+SynFeat and BiLSTM+SynFeat+ILP) performs similarly to or better than the LSTM+ method proposed by<cite> Filippova et al. (2015)</cite> , in terms of both F1 and accuracy. | differences |
f4becae9cd7eeaa7fd3085ff904aaa_24 | In order to evaluate whether sentences generated by our method are readable, we adopt the manual evaluation procedure by<cite> Filippova et al. (2015)</cite> to compare our method with LSTM+ and Traditional ILP in terms of readability and informativeness. | uses |
f4becae9cd7eeaa7fd3085ff904aaa_25 | Our work is based on the deletion-based LSTM model for sentence compression by<cite> Filippova et al. (2015)</cite> . | uses |