SlowGuess/NAACL_2022_OCR huggingface dataset:
Datasets. We evaluate our HiRAM on DSRE benchmarks, New York Times - NYT (Riedel et al., 2010), including NYT-520K and NYT-570K. NYT datasets have 53 distinct relations, including an NA class denoting the unavailable relation between entity pairs. Each relation includes two coarse-grained relations (i.e.,  $M = 2$ ), and the number of relations from fine to coarse are 53, 36 and 9. NYT-520K and NYT-570K have the same testing set containing 172,488 sentences, with 96,678 entity pairs. The only difference is that there is an overlap of 11,416 entity pairs between training and testing in NYT-570K. Thus, NYT-520K has severer wrong labeling and long-tail problems.
P@N (%)	One	Two	All	AUC
100	200	300	Mean	100	200	300	Mean	100	200	300	Mean
Comparative Approaches
CNN+ATT (Lin et al., 2016)	76.2	65.2	60.8	67.4	76.2	65.7	62.1	68.0	76.2	68.6	59.8	68.2	-
PCNN+ATT (Lin et al., 2016)	73.3	69.2	60.8	67.8	77.2	71.6	66.1	71.6	76.2	73.1	67.4	72.2	0.341
CoRA (Li et al., 2020b)	78.0	69.0	66.0	71.0	79.0	72.0	66.3	72.4	81.0	74.0	68.3	74.4	0.344
RESIDE (Vashishth et al., 2018)	80.0	75.5	69.3	74.9	83.0	73,5	70.6	75.7	84.0	78.5	75.6	79.4	-
InSRL (Chu et al., 2020)	-	-	-	-	-	-	-	-	-	-	-	-	0.451
HiRAM	93.0	89.0	83.0	88.3	93.0	88.5	84.0	88.5	93.0	88.5	86.0	89.2	0.484
Ablations
HiRAM w/o Hierarchy in §2.3	88.0	84.5	83.0	85.2	90.0	86.0	85.0	87.0	90.0	86.5	85.0	87.2	0.450
HiRAM w/o CF in §2.1	78.0	75.5	74.3	75.9	87.0	76.5	74.0	79.2	87.0	77.5	74.7	79.7	0.425
HiRAM w/o Rel Guidance in Eq. 16	89.0	86.0	76.7	83.9	93.0	88.0	81.7	87.6	93.0	87.0	86.7	88.9	0.482
HiRAM w/ TC	84.0	82.0	75.3	80.4	85.0	81.5	79.7	82.1	89.0	82.5	78.0	83.2	0.462
RoBERTa (Liu et al., 2019)	44.0	46.5	43.3	44.6	38.0	39.5	38.7	38.7	33.0	36.5	37.7	35.7	0.301
RoBERTa w/ CF	80.0	76.0	74.0	76.7	81.0	78.5	76.0	78.5	81.0	76.0	75.0	77.3	0.488
RoBERTa w/ HiRAM	85.0	83.0	79.3	82.4	86.0	85.5	81.3	84.3	89.0	86.0	81.7	85.6	0.518
Table 1: Model Evaluation and ablation study on NYT-520K. “P@N” denotes precision values for the entity pairs with the top-100, -200 and -300 prediction confidence by randomly keeping one/two/all sentence(s) in each bag. The abbreviation “CF” represents Context-Free embedding in §2.1; “TC” represents Type Concatenation replacing CF. “RoBERTa” directly predicts relations via [CLS] token. “RoBERTa w/ CF” adds context-free type-enriched word embedding module on the output of RoBERTa to generate sentences representation. “RoBERTa w/ HiRAM” denotes the combination of HiRAM and RoBERTa.
P@N (%)	One	Two	All	AUC
100	200	300	Mean	100	200	300	Mean	100	200	300	Mean
Comparative Approaches
PCNN+HATT (Han et al., 2018)	84.0	76.0	69.7	76.6	85.0	76.0	72.7	77.9	88.0	79.5	75.3	80.9	0.42
PCNN+BAG-ATT (Ye and Ling, 2019)	86.8	77.6	73.9	79.4	91.2	79.2	75.4	81.9	91.8	84.0	78.7	84.8	0.42
SeG (Li et al., 2020a)	94.0	89.0	85.0	89.3	91.0	89.0	87.0	89.0	93.0	90.0	86.0	89.3	0.51
CoRA (Li et al., 2020b)	94.0	90.5	82.0	88.8	98.0	91.0	86.3	91.8	98.0	92.5	88.3	92.9	0.53
HiRAM	96.0	91.5	85.7	91.1	98.0	94.5	89.3	93.9	98.0	95.0	92.3	95.8	0.580
Table 2: Model Evaluation on NYT-570K, published by PCNN+HATT (Han et al., 2018)
Evaluation Metrics. Following previous works (Lin et al., 2016; Han et al., 2018; Zhang et al., 2019; Li et al., 2020b; Chu et al., 2020), we use area under precision-recall curve (AUC) and top-N precision  $(\mathrm{P}@\mathrm{N})$  to measure models' performance with the disturbance of wrong labeling, and use Hits@K to measure the performance on long-tail relations. AUC measures the ability of relation classification, while  $\mathrm{P}@\mathrm{N}$  measures the precision of high-confidence predictions ranked by the model.
Settings. For both versions of NYT datasets,  $d_{e}$ ,  $d_{p}$ ,  $d_{w}$ ,  $d_{h}$  and  $M$  are 50, 5, 60, 690, and 2 respectively. The types number of each entity is various but we set an upper limit and pad BLANK as a choice. We use AdaDelta (Zeiler, 2012) with 0.1 learning rate. Batch size is 160 with 15 epochs and 5-th is the best, dropout probability is 0.5, weight decay of L2-reg is  $10^{-5}$ . We use random initialization or RoBERTa-base to initialize our models.
Comparative Approach. We compare our HiRAM with many strong competitors, including (1) PCNN+ATT (Lin et al., 2016) proposes a se
lective attention to alleviate wrong labeling. (2) PCNN+HATT (Han et al., 2018) extends selective attention with hierarchical relations. (3) RE-SIDE (Vashishth et al., 2018) leverages side KGs' information to improve DSRE. (4) PCNN+BAG-ATT (Ye and Ling, 2019) proposes intra-bag and inter-bag attentions to handle the wrongly labeled sentences. (5) PCNN+KATT (Zhang et al., 2019) integrates externally pre-trained graph embeddings with relation hierarchies for long-tail relations. (6) SeG (Li et al., 2020a) focuses on one-sentence bags and proposes entity-aware embedding. (7) CoRA (Li et al., 2020b) transfers multi-granular relations features into sentences in hierarchies for long-tail relations. (8) InSRL (Chu et al., 2020) integrates sentence, entity description and types together via intact space representation learning.
3.1 Overall Performance on Benchmarks
As shown in Tables 1 and 2, HiRAM outperforms former baselines on NYT-570K. Different from CoRA's poor performance on NYT-520K, HiRAM achieves a new state-of-the-art on both popular
# Training Instance	<100	<200
Hits@K (Macro)	10	15	20	10	15	20
PCNN+ATT (Lin et al., 2016)	<5.0	7.4	40.7	17.2	24.2	51.5
PCNN+HATT* (Han et al., 2018)	29.6	51.9	61.1	41.4	60.6	68.2
PCNN+KATT* (Zhang et al., 2019)	35.3	62.4	65.1	43.2	61.3	69.2
CoRA* (Li et al., 2020b)	66.6	72.0	87.0	72.7	77.3	89.4
CoRA (Li et al., 2020b)	66.6	66.6	75.9	71.7	72.7	80.3
HiRAM	72.2	96.3	96.3	77.3	96.9	96.9
HiRAM w/o Hierarchy in §2.3	50.0	88.9	92.6	59.1	90.9	93.9
HiRAM w/o CF in §2.1	66.6	88.9	92.6	72.7	90.9	93.9
HiRAM w/o Rel Guidance in Eq. 16	55.6	66.7	88.9	63.6	72.7	90.9
HiRAM w/ TC	72.2	77.7	88.9	77.3	81.8	90.9
RoBERTa (Liu et al., 2019)	0	0	0	0	0	11.6
RoBERTa w/ HiRAM	38.8	61.1	66.6	50.0	54.5	72.7
Table 3: Hits@K (Macro) tests only on the relations whose number of training instance  $< {100}/{200}$  . "Hits@K" denotes whether a test sentence bag whose gold relation label  ${r}^{\left( 0\right) }$  falls into top-  $K$  relations ranked by their prediction confidence. "Macro" denotes macro average is applied regarding relation labels. " * " denotes the model is trained on NYT-570K.
Case Sentence 1: although the regime of president bashar_al-assad hails from an obscure offshoot of shiism – the alawites – syria is nearly three-quarters Sunni, with alawites, members of other muslim sects and ...
r(2): /people	r(1): /people/person	r(0): /people/person/religion
Case Sentence 2: having so many operating systems makes it expensive to make software , said faraz_hoodbhoy, the chief executive of camera phones save and share multimedia content.
r(2): /business	r(1): /business/company	r(0): /business/company/founder
Table 4: Two cases with long-tail relations are mis-classified by previous works whereas HiRAM is competent. Analysis of the attention probability shown in Figure 3 proves the utility of context-related type-sentence alignment with relation guidance.
benchmarks in  $\mathrm{P@N}$  and AUC. Compared with InSRL integrating both clean entity types' concatenation and accurate entity descriptions, HiRAM increases the AUC score by nearly  $7\%$ , verifying the capability of our specific model designer.
3.2 Ablation Study
We conduct an ablation study on NYT-520K, as shown at the bottom of Table 1. Compared to HiRAM, "HiRAM w/o Hierarchy" drops  $6\%$  in AUC. "HiRAM w/o Rel Guidance" performs well on  $\mathbb{P}@\mathbb{N}$  and AUC but has huge gap in  $\mathbb{P}@\mathbb{O}\mathbb{I}$  one, which represents that the relation-Guided alignment in hierarchy can empower sentence representation with less data in Multi-instance Learning. Meanwhile, top-n precision of "HiRAM w/o CF" drops by nearly  $10.5\%$ . To prove the superiority of our specific design, we replace the pairwise type in §2.1 with simple type concatenation. The AUC score of "HiRAM w/ TC" decreases by  $4.5\%$  and nearly  $5.6\%$  of top-n precision. To further emphasize our word embedding §2.1 is module-agnostic, we combine RoBERTa (Liu et al., 2019) with our module respectively. As the bottom panel shows, "RoBERTa w/ CF" makes great progress, and "RoBERTa w/ HiRARM" achieves the best
performance among three RoBERTa-related experiments. However, due to the strong ability of RoBERTa model, the wrong labeling problem hurt the performance severely, especially in  $\mathrm{P@N}$ .
3.3 Performance on Long-Tail Relations
Since former baselines are mainly trained on NYT-570K, we reproduce CoRA on NYT-520K for fair comparison as shown in Table 3. HiRAM achieves a new state-of-the-art result in Hits@K with  $20\%$  superiority. Removing hierarchy module in §2.3, the performance of "HiRAM w/o Hierarchy" decreases by nearly  $30\%$  on Hits@10 but is better than baselines in other settings, verifying the importance of hierarchical model for long-tail relations. The huge decline of "HiRAM w/o Rel Guidance" verifies the necessity of relation guidance. Due to lacks of plenty reliable training data, RoBERTa is hard to handle the long-tail problem but our specific modules further increase its performance.
3.4 Case Study and Error Analysis
Firstly, we conduct a case study to qualitatively analyze the effect of our model in §2.3 The case study of two samples are shown in Table 4 and the type-sentence alignment distribution is shown in
Figure 3. Secondly, we investigate the possible reasons for the misclassifications of HiRAM.
Figure 3: Each heatmap represents the distribution of type-sentence alignment  $\mathbf{a}$  in Eq.(10) and  $\mathbf{a}^l$  in Eq.(17). The horizontal axis represents the types of subject entity, and the vertical axis represents the types of object entity. The top row, from left to right, represents three alignment distributions of first case, and the bottom row represents three alignment distributions of second case, as Table 4 shows. Notice that "VC" is the abbreviation of venture capitial.
Distribution of Type-Sentence Alignment. For the first case, despite the failure in expressing the long-tail relation “/PEOPLE/PERSON/RELIGION”, the selected pairwise types are sufficient to predict this relation. As the top row of Figure 3 shows, people.person with BLANK helps to identify the character of subject entity, and religion.religion with high alignment score can provide direct attributes. For the second case, the semantics is implicitly related to its long-tail relation “/BUSINESS/COMPANY/FOUNDER”. The proper pairwise types are selected by coarser relation guidance, like (organizerOrganizer, organizer.founder).
Error Analysis. To analyse the implicit reasons for wrong predictions, we have manually checked several randomly-sampled error test examples. 1) Most of error cases are annotated as /PEOPLE/PERSON/PLACE_OF_BIRTH because the semantics and the relation may be completely irrelevant and the types are hard to maintain people's birth place. 2) Mean pooling in Eq.(4) might not be the most optimal way to replace entity itself when the entity has too many characters.
Wrong Labeling Problem. Many works (Liu et al., 2016; Ji et al., 2017; Ye and Ling, 2019; Li et al., 2020a) propose various extensions of vanilla selective attention (Lin et al., 2016). Ye and Ling
(2019) combine intra-/inter-bag level selective attention for DSRE. For one-sentence bags, Li et al. (2020a) design the entity-aware embedding in a context-free manner with a gate mechanism.
Long-tail Relations. Knowledge transfer via hierarchical relations is effective. Han et al. (2018) design relation-to-sentence attention in hierarchies, and Li et al. (2020b) modify it to sentence-to-relation attention. Many works (Vashishth et al., 2018; Hu et al., 2019; Chu et al., 2020) resort to extra knowledge, i.e., entity description and entity types. Entity description (Hu et al., 2019; Chu et al., 2020) mainly stems from the Wikipedia page, which contains factual statements of the relation with other entities. Such oracle knowledge can boost DSRE performance but is impractical.
In this work, we propose a new model, HiRAM, training on a single Titan XP, except for RoBERTa w/ RTX 6000, to alleviate wrong labeling and long-tail problems in DSRE. For the wrong labeling problem, we propose a context-free type-enriched word embedding to enrich each word with prior knowledge and a context-related type-sentence alignment module to complement sentences with semantics-fitted pairwise types. For the long-tail problem, we extend the base alignment into the hierarchy to utilize the multi-granular entity types. The experiments with extensive analyses show the superiority of HiRAM.
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